{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "818ee536",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:23.900170Z",
     "iopub.status.busy": "2023-01-08T20:59:23.899327Z",
     "iopub.status.idle": "2023-01-08T20:59:25.360356Z",
     "shell.execute_reply": "2023-01-08T20:59:25.359147Z"
    },
    "papermill": {
     "duration": 1.470691,
     "end_time": "2023-01-08T20:59:25.363450",
     "exception": false,
     "start_time": "2023-01-08T20:59:23.892759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d492de4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:25.373592Z",
     "iopub.status.busy": "2023-01-08T20:59:25.372606Z",
     "iopub.status.idle": "2023-01-08T20:59:25.555316Z",
     "shell.execute_reply": "2023-01-08T20:59:25.553810Z"
    },
    "papermill": {
     "duration": 0.190799,
     "end_time": "2023-01-08T20:59:25.558195",
     "exception": false,
     "start_time": "2023-01-08T20:59:25.367396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21613, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520 2014-10-13  221900.0         3       1.00         1180   \n",
       "1  6414100192 2014-12-09  538000.0         3       2.25         2570   \n",
       "2  5631500400 2015-02-25  180000.0         2       1.00          770   \n",
       "3  2487200875 2014-12-09  604000.0         4       3.00         1960   \n",
       "4  1954400510 2015-02-18  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### IMPORT DATA ###\n",
    "\n",
    "df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "868d06d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:25.568426Z",
     "iopub.status.busy": "2023-01-08T20:59:25.567927Z",
     "iopub.status.idle": "2023-01-08T20:59:26.192752Z",
     "shell.execute_reply": "2023-01-08T20:59:26.191579Z"
    },
    "papermill": {
     "duration": 0.633618,
     "end_time": "2023-01-08T20:59:26.195921",
     "exception": false,
     "start_time": "2023-01-08T20:59:25.562303",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIqKywUAGEREREREREZUNBjKIiIiIiIiIqGwwkEFEREREREREZYOBDCIiIiIiIiIqGwxkEBEREREREVHZYCCDiIiIiIiIiMoGAxlEREREREREVDYYyCAiIiIiIiKissFABhERERERERGVDQYyiIiIiIiIiKhsMJBBRERERERERGWDgQwiIiIiIiIiKhsMZBARERERERFR2WAgg4iIiMgiERkuIg+IyOsi0iYiny52mYiIiAYbR7ELQERERFRGfg3gCVW9RERcAIYUu0BERESDDQMZRERERBaIyFEAzgEwHwBU1QfAV8wyERERDUYcWkJERERkzTgAXgB3ikiriPxeRKqKXSgiIqLBhoEMIiIiImscAOoA/FZVawF0Afhh9AoislBENonIJq/XW4wyEhERDXgMZBARERFZsxfAXlV9Kfz7AwgFNiJUdaWq1qtqvcfj6fcCEhERDQYMZBARERFZoKr/BLBHRE4JLzoPwPYiFomIiGhQYrJPIiIiIuuWALg3PGPJ2wCuLHJ5iIiIBh0GMoiIiIgsUtWtAOqLXQ4iIqLBjENLiIiIiIiIiKhsMJBBRERERERERGWDgQwiIiIiIiIiKhsMZBARERERERFR2WAgg4iIiIiIiIjKBgMZRERERERERFQ2GMggIiIiIiIiorLBQAYRERERERERlQ0GMoiIiIiIiIjyxNvlRUtHC7xd3mIXZcBiIIOIiIiIiIgoD5pebcLY5WMx454ZGLt8LJq2NRW7SAMSAxlEREREREREOfJ2edHwaAO6/d041HsI3f5uNKxtYM+MAmAgg4iIiIiIiChH7Qfb4bK7YpY57U60H2wvToEGMAYyiIiIiIiIiHJUM7wGvoAvZllfoA81w2uKU6ABjIEMIiIiIiIiohx5qjxonN0It8ONYRXD4Ha40Ti7EZ4qT7GLNuA4il0AIiIiIiIiooFg7sS5mD5uOtoPtqNmeA2DGAXCQAYRERERERFRnniqPAxgFBiHlhARERERERFR2WAgg4iIiIiIiKiMebu8aOloGTRTvTKQQURERERERFSmml5twtjlYzHjnhkYu3wsmrY1FbtIBcdABhEREREREZWtwdYbIZq3y4uGRxvQ7e/God5D6PZ3o2Ftw4Cvi4IGMkRkuIg8ICKvi0ibiHxaRI4WkadE5M3w/0eE1xUR+Y2I7BCRV0SkLmo788Lrvyki8wpZZiIiIiIiIioPg7E3QrT2g+1w2V0xy5x2J9oPthenQP2k0D0yfg3gCVU9FcAZANoA/BDAX1X1JAB/Df8OABcCOCn8byGA3wKAiBwN4CYAnwIwFcBNRvCDiIiIiIiIBqfB2hshWs3wGvgCvphlfYE+1AyvKU6B+knBAhkichSAcwA0AoCq+lT1IIDZAFaHV1sNYE7459kA7taQFwEMF5GRAM4H8JSqfqCqBwA8BeCCQpWbiIiIiIiISt9g7Y0QzVPlQePsRrgdbgyrGAa3w43G2Y0DfvpXRwG3PQ6AF8CdInIGgM0AvgPgOFXdF17nnwCOC/88CsCeqPfvDS9LtpyIiIiIiIgGqcHaGyHe3IlzMX3cdLQfbEfN8JoBH8QACju0xAGgDsBvVbUWQBeODCMBAKiqAtB87ExEForIJhHZ5PUOnq5EREREREREg9Fg7Y1gxlPlwZRRUwbNsReyR8ZeAHtV9aXw7w8gFMh4V0RGquq+8NCR98KvdwA4Pur9o8PLOgCcG7f86fidqepKACsBoL6+Pi/BESIiIiIiIipdg7E3AhWwR4aq/hPAHhE5JbzoPADbATwKwJh5ZB6AteGfHwXwjfDsJWcCOBQegvIkgJkiMiKc5HNmeBkRERERERENcsXojTCYp3wtBYXskQEASwDcKyIuAG8DuBKh4Ml9ItIAYBeAS8PrrgMwC8AOAIfD60JVPxCR/wegJbzeT1T1gwKXm4iIiIiIiChB06tNaHi0AS67C76AD42zGzF34txiF2tQKWggQ1W3Aqg3eek8k3UVwLVJtrMKwKq8Fo6IiIiIiIgoA9FTvnb7uwEADWsbMH3cdA5r6UeFTPZJRERERERENGBwytfSUOihJUREREQDhoi0A/gIQACAX1XNep4SEdEAxSlfSwN7ZBARERFlZpqqTmIQg4ho8OGUr6WBPTKIiIiIiIiILOKUr8XHQAYRERGRdQpgvYgogDtUdWWxC0RERP3PU+VhAKOIGMggIiIisu5sVe0QkX8B8JSIvK6qzxgvishCAAsBYMyYMcUqIxER0YDGHBlEREREFqlqR/j/7wF4GMDUuNdXqmq9qtZ7PHxSR0REVAgMZBARERFZICJVIjLU+BnATADbilsqIiKiwYdDS4iIiIisOQ7AwyIChO6h/qiqTxS3SERERIMPAxlEREREFqjq2wDOKHY5iIiIBjsOLSEiIiIiIiKissFABhERERERERGVDQYyiIiIiIiIiKhsMJBBRERERERERGWDgQwiIiIiIiLKC2+XFy0dLfB2eYtdFBrAGMggIiIiIiKinDW92oSxy8dixj0zMHb5WDRtayp2kWiAYiCDiIiIiIiIcuLt8qLh0QZ0+7txqPcQuv3daFjbwJ4ZVBAMZBAREREREVFO2g+2w2V3xSxz2p1oP9henALRgMZABhEREREREeWkZngNfAFfzLK+QB9qhtcUp0A0oDGQQURERERERDnxVHnQOLsRbocbwyqGwe1wo3F2IzxVnmIXjQYgR7ELQEREREREROVv7sS5mD5uOtoPtqNmeA2DGFQwDGQQERERERFRXniqPIM+gOHt8loO5mSyLh3BoSVEREREREREeZDJFLScrjZ7DGQQERERERER5SiTKWg5XW1uLAUyROT0QheEiIiIiIiIqFxlMgUtp6vNjdUeGbeJSLOILBKRowpaIiIiIiIiIqIyk8kUtJyuNjeWAhmq+lkAXwNwPIDNIvJHEZlR0JIRERERERERlYlMpqDldLW5sTxriaq+KSL/H4BNAH4DoFZEBMC/q+pDhSogERERERERUTnIZApaTlebPUuBDBH5JIArAXwBwFMAvqiqW0Tk4wD+DoCBDCIiIiIiIhr0MpmCltPVZsdqj4wVAH6PUO+LbmOhqr4T7qVBRERERERERFRwVgMZXwDQraoBABARG4BKVT2sqvcUrHRERERERERERFGszlqyAYA76vch4WVERERERERERP3GaiCjUlU7jV/CPw8pTJGIiIiIiIiIBiZvlxctHS3wdnmLXZSyZTWQ0SUidcYvIjIZQHeK9YmIiIiIiIgoStOrTRi7fCxm3DMDY5ePRdO2pmIXqSxZzZFxHYD7ReQdAALgYwAuK1ShiIiIiIiIiAYSb5cXDY82oNvfjW5/qF9Aw9oGTB83nTOXZMhSIENVW0TkVACnhBe9oap9hSvW4CO3SEbr601aoJIQERERERFRvrUfbIfL7ooEMQDAaXei/WA7AxkZstojAwCmAKgJv6dORKCqdxekVEREREREREQDSM3wGvgCvphlfYE+1AyvKU6BypilHBkicg+AXwA4G6GAxhQA9QUsFxEREREREdGA4anyoHF2I9wON4ZVDIPb4Ubj7Eb2xsiC1R4Z9QAmqCrHMxAREdGAICJjAZykqhtExA3AoaofFbtcREQ0cM2dOBfTx01H+8F21AyvYRAjS1YDGdsQSvC5r4BlISIiIuoXIvItAAsBHA3gEwBGA7gdwHnFLBcREQ18nioPAxg5shrIOBbAdhFpBtBrLFTVLxWkVERERESFdS2AqQBeAgBVfVNE/iXdm0TEDmATgA5VvaiwRSQiIkrO2+UdtD07rAYybi5kIYiIiIj6Wa+q+kRCs4aJiAOAlSG03wHQBmBYActGRESUUtOrTWh4tAEuuwu+gA+Nsxsxd+LcYher31hK9qmq/wegHYAz/HMLgC0FLBcRERFRIf2fiPw7ALeIzABwP4DHUr1BREYD+AKA3/dD+YiIiEx5u7xoeLQB3f5uHOo9hG5/NxrWNsDb5S120fqN1VlLvgXgAQB3hBeNAvBIgcpEREREVGg/BOAF8CqAqwCsA/D/pXnPcgDXAwgmW0FEForIJhHZ5PUOnhtKIiLqP+0H2+Gyu2KWOe1OtB9sL06BisBSIAOhcaRnAfgQCI0jBZB2HCkRERFRiXIDWKWqX1XVSwCsCi8zJSIXAXhPVTen2qiqrlTVelWt93gG13hlIiLqHzXDa+AL+GKW9QX6UDO8pjgFKgKrgYxeVY3UVAbjSImIiIhK0V8RG7hwA9iQYv2zAHxJRNoBrAHweRH5Q+GKR0REZM5T5UHj7Ea4HW4MqxgGt8ONxtmNgyrhp9Vkn/HjSBchzThSIiIiohJWqaqdxi+q2ikiQ5KtrKo3ALgBAETkXADfV9WvF7qQRESlYjDPkFGK5k6ci+njpg/ac2I1kPFDAA2IHUfKRFdERERUrrpEpE5VtwCAiEwG0F3kMhERlaTBPkNGqfJUeQZdAMNgKZChqkEAvwv/IyIiIip31wG4X0TeASAAPgbgMitvVNWnATxdqIIRERVDsh4X0TNkdPtD8d6GtQ2YPm76gG9EsxdK6bIUyBCRnTDJiaGqJ+S9REREREQFpqotInIqgFPCi95Q1b5ilomIqFhS9bgwZsgwghjAkRkyBnLjnr1QSpvVoSX1UT9XAvgqgKPzXxwiIiKiwhGRz6vq30Tk4riXThYRqOpDRSkYEVGRpOtxMRhnyBjMvVDKhaVZS1T1/ah/Haq6HMAXCls0IiIiorz7XPj/XzT5d1GxCkVEVCxGj4toRo8LYHDOkJGuTqj4rA4tqYv61YZQDw2rvTmIiIiISoKq3iQiNgCPq+p9xS4PEVGxWelxMdhmyMimFwrzafQvSz0yAPwy6t9/ApgM4NJCFYqIiIioUMJJzK8vdjmIiEqB1R4XnioPpoyaMiga6Zn2Qml6tQljl4/FjHtmYOzysWja1tTPJR58RDUhh2fZq6+v102bNhW1DHKLFHT7etPAO29ERFR4IrJZVevTrzmwich/AdgP4E8AuozlqvpBvvZRCvcjNPDwqS8VSrldW/1RXiv78HZ5MXb52JhkqG6HG7uu25X3cpXbOUol1/sRq0NLvpvqdVX9VbYFICIiIiqCyxCakW1R3HLOyEYli7MoUCF5qjxl0zjur8+ClTrJ96wuyYIV/PzHsjq0pB7ANQBGhf9dDaAOwNDwPyIiIqJyMgHA/wJ4GcBWACsAnFbMAhGlEj2LwqHeQ+j2d6NhbQO8Xd6st9fS0ZL1+4mKJd+fhVzlc1aXZENUSu2YS4HVQMZoAHWq+j1V/R5COTLGqOotqnpL4YpHREREVBCrAYwH8BuEghgTwsuISlI+Z1HgeH4qZ6U2o0i+ZnVJFawotWMuBVZnHjkOQHSYyRdeRkRERFSOJqrqhKjfN4rI9qKVhiiNfD31jW4sGV3hG9Y2YPq46WUzrIAGt3z2gMiXfMzqkmqISs3wGhz2HY5Zv9vXXdRjLjarPTLuBtAsIjeLyM0AXgKfWhAREVH52iIiZxq/iMinADAzJ5WsfD315ZNdKnf5+iwUoly5zOqSLkAjttjJJOJ/H2ws9chQ1Z+KyOMAPhtedKWqthauWEREREQFNRnACyKyO/z7GABviMirAFRVP1m8ohGZy8dT31J8mk2UqXx8FkqNEaBpWNsAp92JvkBfJEDT0tECt8Md89mtdFRmnVB0ILA6tAQAhgD4UFXvFBGPiIxT1Z2FKlipKfR0qkRERNSvLih2AYiykevMEqkaS0TlpJxmWbEqWYCGAchEVqdfvQmhmUtOAXAnACeAPwA4q3BFIyIiIioMVd1V7DIQFctAfJpNNFCYBWgYgExktUfGlwHUAtgCAKr6johYmnZVROwIjTntUNWLRGQcgDUAjgGwGcAVquoTkQqEcnFMBvA+gMtUtT28jRsANAAIAPi2qj5psdxERERERBRnID7NJhrI+isAacySUupBTqvJPn2qqgAUAESkKoN9fAdAW9TvPwewTFVPBHAAoQAFwv8/EF6+LLweRGQCgMsRmtv9AgC3hYMjRERERESUhLfLi5aOFni7vMUuSl4N1OMiAlJf37kmFE2nnKZmthrIuE9E7gAwXES+BWADgN+le5OIjAbwBQC/D/8uAD4P4IHwKqsBzAn/PBtHZkJ5AMB54fVnA1ijqr3hnBw7AEy1WG4iIiIiokGnnBokmRiox0UEFPf6jp6a+VDvIXT7u9GwtqFkA4ZpAxnhYMKfEAouPIhQnowbVXWFhe0vB3A9gGD492MAHFRVf/j3vQBGhX8eBWAPAIRfPxReP7Lc5D1ERERERBSl3BokVg3U4yICsru+89k7qdymZk4byAgPKVmnqk+p6r+p6vdV9al07xORiwC8p6qb81FQC/tbKCKbRGST18svMyIiIiIanMqtQWLVQD0uIiDz6zvfvTfKbWYUq0NLtojIlAy3fRaAL4lIO0LJPT8P4NcIDU8xkoyOBtAR/rkDwPEAEH79KISSfkaWm7wnQlVXqmq9qtZ7PKWblISIiIioPzGfwOBTbg0Sq3I9Ln4Wyls5nb9syprJ9V2I3knGzChuhxvDKobB7XCX9MwoVgMZnwLwooi8JSKviMirIvJKqjeo6g2qOlpVaxBK1vk3Vf0agI0ALgmvNg/A2vDPj4Z/R/j1v4V7gzwK4HIRqQjPeHISgGaL5SYiIiIatJhPYHAqtwaJVbkcFz8L5a2czp9R1mmrp2VU1kyu70L1Tpo7cS52XbcLG67YgF3X7cLciXNz2l4hSShWkORFkTGqultExpq9bnUOdhE5F8D3w9OvnoBQD42jAbQC+Lqq9opIJYB7EJrm9QMAl6vq2+H3/wjAAgB+ANep6uOp9ldfX6+bNm2yUjTL5BbJ6/ZypTclP29ERETJiMhmVa0vdjkGg0Lcj2TC2+XF2OVj0e3vjixzO9zYdd2usm/QkjXlMo1ipjI9Ln4Wyls5nT9vlxejl42O6Vnhsruwd+ley2W1cn2XU50kk+v9iCPN648AqFPVXSLyoKp+JZudqOrTAJ4O//w2TGYdUdUeAF9N8v6fAvhpNvsmIiIiGoyMJ3bRN7rGE7tyudGl3HiqPAPyXGd6XPwslLdyOn+t+1oThof4Aj607mvFzBNnWtpGqus7OsjROLsRDWsb4LQ70RfoGxC9rjKRLpAR3Q3hhEIWhIiIiIjyp9B5Egbq0/5iYF0W1kDNGTJYWDl/uXyGyuXz1/RqExoebYDL7oIv4EPj7Ebsum5XWZS9ENLlyNAkPxMRERFRCStknoRyGq9e6liXhTdQc4YMFunOXy6foXx//mpH1sIpzphlTnGidmRtTttNltxz/+H9OW23nKXLkREA0IVQzww3gMPGSwjNzDqs4CXMAnNkEBERmWOOjP5T7BwZhnw/bRwIY7NLBeuyf5XLk3cyZ3b+cvkMFerz17StCQseWQC7zY5AMIBVc1blnDSzpaMFM+6ZgUO9h2LKGtQgKh2VkR4apZycM15Bc2Soqj3bDRMRERFR8eU7T0I5jVcvdazL/jVQc4YMFmbnz+pnyCwIYvZeh82BdW+uw6yTZlkKhJgFxuZOnIvp46bnJWhm7KPaVZ0wvMYod2+gFwDQsLYB08dNHzTXuNXpV4mIiIiImG8gj1iXRLmx8hlKNnzE7L0f+T7CkseXpB1mkm5IiqfKgymjpsT0HGnpaIG3y2v52KL3MXnlZDTUNUSG11TYK+C2u2PWz8f0q+WEgQwiIiIiSsu4EQfAfAN5wtwNhZdNA5LKR7rPULLcEt4ub8x7h7qGRrb5ke+jmPXipdqmmWzycJjto3FLIzYv3IwNV2xA61WtsdNyYPAFQdPNWkJEREREgxyz5RdOPruhUyyz67accgiQNak+Q+mGnhjvXffmOix5fAk+8n1kul60TIaERQckjPWtDAFJto9OXyemjJoCAJx+tdgFICIiIioHIlIJ4BkAFQjdQz2gqjcVt1SFl+xGfNd1uyI31P1ZlmI2+Au1f+ZuyL9sG5BUulJ9/pJ9hqwMPfFUeTDrpFm45i/XpFwvk20ass2Dk2wf1a5qtHS0oGZ4TUFycRjHUA6BVQ4tISIiIrKmF8DnVfUMAJMAXCAiZxa3SIVn3IhHK8ZY7GJPU1rs/VNmSuW6pfzI9vNndfhWJsO8rKxrDGkyS9JpZQiI2T4a6howeeXkmDqIz8WRjei6Hb1sNEb9clRZfM+lnH61XHH6VSIiInOcfjU/RGQIgOcAXKOqL5mtUyrTr+aqFKYIzXcZMu1ZUQp1QJnhORs48nEurX7m27xtaO5oxtRRUzHeMz6rbcYPaWqoa0DjlsaYISBWe1JEz1oyeeXkjOrAyrGY1W20Qn5mcr0fYY8MIiIiIotExC4iWwG8B+Cp+CCGiCwUkU0issnrHRjJBUshIWU+n65n+mTX2+XFujfXwWGLHZHNp/ulrRSuW8qPfHz+rfRcaHq1CZNXTsZ3nvgOJq+cnPa7wWyb6ZJ07rpuF6Cw/B1k7KPT15lRHSxZtwQTbpuA+WvnY8JtE7Dk8SWm65nVrdV9FBtzZBARERFZpKoBAJNEZDiAh0Vkoqpui3p9JYCVQKhHRnFKmX/FTkiZr2lKreRNiH7KuuHtDWh4tAEOmyMmCWC2+6f+VezrdjApZP4aK5//XPefr5wq6ZJ0ZrufTL4D27xtuLXl1phltzbfikX1ixJ6Zpht18o+SgF7ZBARERFlSFUPAtgI4IIiF6XfxD997M9pLfP1dD3dk93o3hpjlo3B/LXz0e3vjgliVLuq+XS/jOQjh0CmBtuUr4XOH5Pu859NL6v485OvXl/pAg7Z7idZHQBIOJbmjmbTbZgt91R50FDbELPMBltZ9GJijwwiIiIiC0TEA6BPVQ+KiBvADAA/L3KxiqIY01rm4+l6qkaG2ZPSeENdQ7HiwhWYddKskr25p+Iq9Slf891zor9mh0n2+c90/8nOT756fRkBh2TTouayn/g62PD2BoxdPjbhWKaOmmr6frPl3i4vGlsbY5ZVOCpw/yX3o3ZkbUl/z7FHBhEREZE1IwFsFJFXALQglCPjz0UuU78zGwPesLah33pm5PJ0PdWT3XRjxQHAH/QziEFJFfOzYUUhek705+wwZp//TPaf6vzkM6fK3Ilzseu6XZGcGNGBrFz3Y9QBgKTHMt4zHounLo553+Kpi00TfiarvxHuESX/PcceGUREREQWqOorAGqLXY5iSzYGvP1ge8nf+ALJn+yaPSl1ihMOu8P0ySpQ2LwAVH5K+bNRqJ4T+erJ0B/7T3d+8plTxVPlSfr+fOwn3bGsuHAFFtUvSjtrSbHPXy4YyCAiIiIiy8r5xtdg1shI1iU8WYOj1IcQUP8r5c9GoYIs6YZSFFom+7dyflIFIPIp1/1YOZbxnvFpp5At9vnLhagOmITaEYWYt11ukbxuL1d608A7b0REVHi5zttO1hXifqRUNG1rSrjxHSiNeCu9LLxdXoxdPjamUeh2uLHrul1l0QAoB+Xa26VUPxuFvmaLfb7S7d94fcs/t2DpE0tL6vxkW3f5vNaKcf5yvR9hjwwiIiKiQSybG9iBPK2llSelpTyEYCAo594upfrZKPST9/7oyZDquyrV/uOvp2XnL0PdyLqSOD+5XOv9NRSmVLFHhkXskUFERAMBe2T0n3LokVHODcZiYo+M7JRab5di9yIohnI95my/q4r1Wc3lWt+8cDM6fZ0xU7aW2/mygj0yiIiIiChj/TVt4kBUzuPKi8VqQ7S/ersM1iBeKTx5zzSYkst3VTF6T+Vyrasqau+oRaWjEt3+bmhQMcQ1ZFBdo1Zx+lUiIiKiQag/p00ciFJNsUixMpmWtJAJM71dXrR0tKDN21bS06QOZNlMAZvLd1V/J2DN9VrvCfSgN9CLQ72H4Av40Kd9vEaTYCCDiIiIaBDK9AbfaATyRvoIT5UHU0ZNKfoT7lKXSUPU6O3idrgxrGIY3A53Xnq7RDega+9InEWZQbzCy6SRHy2XYEShrqdkcrnWK+wVCe+1sp3BikNLiIiIiAahTIZHDNZu+JQfmTZE850w02xoQrxSmSY1G4XKe5Hv7WY7zCPXoVz9mYA1l2vdF/Dh7DvPTrrtcr5GC4GBDCIiIqJBysoNPnNpUK6yaYjmM5eDWQO60l4JhaLCUVHWOU6sBBmzCUgUIniZS8+KXIMRhcgNYlavuVzrLR0tcNvd6A7EBtvcDjcA5HyNlmui12QYyCAiIiIaxFLd4Hu7vFj35jo4bLG3jOU+1Wi+buiL0TAo17IXc1pSswa0iGDLwi2R2SEyKU+pNAitBBmzCUgUKniZa8+KUkhUakhVr9le6zXDawCTiTIFgqAGC1becsUcGURERESUwMgpsOTxJfjI91HMa+XcxTmbZIOF3E4x9lmMsgOxOUXavG1YvXU12rxt/bJfszwJ4z3jM85xUqy6M5MuH0O2OSkKmQi4UEly+zOHj5V6zSZ/TvR1OtQ1NLL8sP8wegI9WSf7zPY6KHUMZBARERFRjOgb3+ggxlDX0IInyytkgyRfN/TZbCfX47KyTyv7KIVGzZJ1SzDhtgmYv3Y+Jtw2AUseX1LwhmimDWiz8pRC3UVLN1Qj24BEf8/0kav+Di71R6BnxYUrYoIZuexjoM5QxUAGEREREcUwu/GtdlVjxYUrCjrVaKEaJEajtHVfa15u6DNtGOTjuNLtM90+8l0H2WrztuHWlltjlt3afCvGLB9T8Iao1afkyeqy1BqE6WbkyDYgUciZPvL9GU8WXGrztsUEovIZKLNSr7nsz1PlwayTZsEf9KfcRz7LW46YI4OIiIiIYpjd+AaCAcw6aVZBe2IUYlx+9NjwXn8vgogda57NDX0mDYNcj8vIx1Dtqk66z3T7iB8f7w/kp4GUjeaOZtPlPf4e9Ph7ABQ3mWyquizFBmGqfAy55KQoRE6TQnzGzRK5AkDtHbVw2BwIahANdQ1o3NKYt/wQ6eo1H/korJ67Nm8bmjuaMXXUVIz3jM96W6WS9yUTDGQQERERUYxck/Jlw8rUjJnebJs1nJzihNvhzum4MqmfbKecBBIbREaDLH6fLR0tSfcBIKEOXHYXKlEJl8PV7zN2TB01Ne06xUwmm+p8TRk1pd8/F1akSoKZS0Ai38k1c/ksJGMWXDK23xvoBRDq8RO9PFXwxOp3TLJ6zWewJt25W7JuSUzvpsVTF2PFhSsy3la5JgJlIIOIiIiIAMTexOfSAMrm6V66p92Z3mwnm3HF7XLj/kvuxwj3CEvlS3YsVusn26f4Zg2ixi2N2Lxwc8JMG6n2YTr1qKMyozrIp/Ge8Vg8dXGkcQkADpsjpht9MXs5pDtfhZx9pVBPxdPNTNRfT+IL0aMlPqjY6++NBDCSSRY8yfQ7Zv/h/dju3Y5qV3Ukge2abWtgt9kt7c/q8Zm9L9kQrUX1i1L2zIjfVjlPr81ABhERERElvYnP9GY226d7qXo5ZHqzbZTBYXOYzrhSO7LW0nGlOxarDcRsnuIne3rd6evElFFTYtZN10PErPFotQ4KYcWFK7CoflGkS/zWd7eWTC8HK71tCjENqHGt2cSGoAb75al4fz+JL1RPr+jg0s4DO3HZg5elXN8seJLpd0x8b4iJnonY5t1mur8ef0/eA3PJhmg1dzQnDWSYKUQvmf4iqlrsMuRdfX29btq0Ka/blFtMJvUtIr1p4J03IiIqPBHZrKr1xS7HYFCI+5FC8XZ5MXb52JibWbfDjV3X7cq4J0au2zF7QtzS0YIZ98zAod5DkfWGVQzDhis2JDTqzcoAhGZc8Qf9lhtruRyLWQMx06f42ew/2dP1pm1NCY3HUus6Xmpj9PuzPN4uL0YvGx0TcHLZXdi7dG9Bc9Lk4zOf7b6t1m02w8lG/XIU+rQvsswGGyocFSmv/0y+Y9q8bZhw2wQrhwogNJyt43sdec1JkawM2xdtzyiQYXYdVNgq8D8z/wfTT5ie0bYylev9CGctISIiIhrkks26UOjZPMyYzSyRSZf0fM24ku2xJJtFAYClGTMM2cwckWxWjkynHi0GqzOK9Jf+LE/rvtaE69sX8KF1X2vB9lnMGVhynT0m3bZXX7walfZKVDmrUGmvxB++8oe0138m3zHJekMk43a5Y+o1k+OKn/3E+P3YIcdi8dTFMesunro448BD/PeMDTb0Bnvx7Se+HZkauVRxaAkRERHRIFftqk7owdDt70a1q9ryNrxdXhzoPoBef+z49HzkO8ikS3q+ZlyxOsVi/FPVfHbVzmc+hkIMhxhMSq3HSK4KOQNLPuoql9wNyT436YKAVr9jrCSsjRZdr2bHtWDtAuzv2p/QAyIh2W9tA36/5few2+wIBANYNWdVzBCtbHtPzJ04F5OOm4SHX38YP/rbj2JeS5d3o5gYyCAiIiIaRMwaGZ2+TrjtbnQHohJC2ivR6eu0tM3oG+4ggqGZQVzurMbA55pc06xBsuyCZZEnopn2iMh0isV8NxAHSwAik8avlSkn86nQuSRqR9bCKc6Y4RBOcaJ2ZG3e9hGvUPkq8lVXuQYEs/ncWP2OGe8Zj5knzMT6t9dHlh0/9Hjs+WiP6foNdQ0pA509/h58+4lvAzgy84hZwCOSkyMQ+t+8h+ah43sdmDdpXkbHGc84Z8lSTmSad6O/MEeGRcyRQUREAwFzZPSfUsyRkayRkct4+WTvfeSyRzJOKJnPBqPRMN6ybwuWPrk0622aNbDT1Vcm0yIm28dAlDSHRwbnPdO6zUeZ+yOXRNO2Jix4ZEHM0/b+GAKUz6BQPuuqmDk80klWtqeueApb9m3B9RuuR4+/J+Y1Y7ahalc1Jt0xKSHYGW37ou3o9HXi042fRkADKcvy5NeexMwTZyYtZ7rvlWQ5heLLU4hABnNkEBEREVFayXI3eLu8WeVjMCQbaz/CPSLjnhjJypcNT5UHNcNrsPTJpTlt02w8f6r8At4uLxpbG2Nea9zSiDZvW8xYd0M2eQCyET/Wvr+3m+w4k513s/pKNuVkm7ctr8cUrb9yScydOBe7l+7GxnkbsXvp7n4JYjS92oTJKyfjO098B5NXTs752stnXRnfSdG5Loo5m020ZMfpsrtw5ugzUWGviHlNVVF7Ry2mrZ6GupV18Af8SKW5oxnvdr2bNoiRitXvFbNjiTbzhJkl2RsDYCCDiIiIaFBI18jINiFkvoZSFKLBWKhGaKpjNtunqmLS7ZMwbfU0jFk2Jm0jPt/BhkIFS6xuN9VxJmtI1d5Rm7DdVFNOFkohc0nE648Eo0bgqc3blvG1ly5olfe6UkAk1Cve+H8pSHWcZq/1BHrQG+hFV18Xevw9CCKYcvtTR01FS0dL2nK47C7T4UeZfK+YlTfa07uezvv3Ub4wkEFEREQ0CNQMr8Fh3+GYZd2+7phGRqqGVLJGjKfKg4bahphl0WPCMylfvhuMhWqEpurBkqwh4wv6Qg2ZQA/mPTQvaSM+n0/7vV1erN+xHgvWLsh7sCSTxlKq4zSrr25/N3oDvQnbTZZkMdPki5nIpbeSmUL1jLEiOvBUe0diAzjVtWclaJXPuoq+vrr6uiLXwfod6wtad1bOT6rjjH8tvndGOkYPiPgpXw0uuwtVziq4HW7cNecuAEgobybfK9HldTvcCa8XevacXDDZJxEREdEgITaJJIozft9/eH/acdSpchgkG0px4zk3ZtSAKUTywUIlNARSz4wQvc+evlAQI1qf9qF1XytqR9YW7Gm/cc5sYkNPoCfmtWxnUYmWSTLGVAGl+Prq9ffCBltM4llju1NGTcHiqYtxa3NsjoxCd33P1+wxhU4amoy3y4vWfa1YsHYBegI9SfMhJLv2rMwgYgTmpo+bjl3X7cq5rsyur25/Ny6+72IENViQujM7P8nOe6prIvq1nQd24rIHL0vYl8vmgsvhSkio/OzuZ+Ht8uK4quNggy2m94YNNjx2+WMY4R6BmuE12PD2BoxdPjbnhMNGeddsWxNJOloOGMggIiIiGgTaD7bD7XDH3ODaYEPtHbWodFQmbVila8SU6nSjhdymIdnMCFYaMsb7CxFoiT5nZvIRLMmksZTuOKPrq9pVjckrJyfd7ooLV+Rlysn+lst0ola2nez6ThnQEidsNhsqHBUpr710n/FCBGiSDXno6usCkL+6M5idn/mPzIcNofoxO65UM6MYr9UMrzGdkWbr1VvR3NGMJY8vwUe+j468FtVTqcJREVPnFY6KSALldNdTpt8rnioPLp94Ob735Pf6dfacXDCQQURERDQIJBvyAAC9gV4A5o2DdI2YZA3aA90HIolEM1GI6UaLMYVpdEPGaOAZose2FyLQYnbOAKDKWRV5mp3rfjJtLKU7zuhzlG674z3j+zWAkY+Gej4DflbLljagpX147hvPwWV3pbz2UgWtChWgib6+bGKLBDAM+ai7aGbnxzhm43sym+PyVHmw+uLVCTPSjPeMx7FDjsU1f7kmZv1kPZXiPwfprqdsvleSlbUUEqyaYSCDiIiIaBCIbxj4g37YxJa2YZXuyXvCUAp/D/wBPy594FJLjb5Sn3o01/J5qjy4a85dkXo3CyTkO9Bids7cDjceuvShjKfETSXTxpLV4yxkL5pM5auhnmu+lmTTAGfaWypapb0SLrsraT4GQ6pGdUtHS0ECNMCR66B1Xytmr5kd06Mk3wlX0yW9NOSzp1kmPZXiPwdWrqdMvleihwbtXrq7JD576TCQQURERDRYaGgGDUjo5wBip/czaxxYefKe0ODQHhzqPQQgdaOvWDkDrMpX+TJtmOcjeGJ2zmaeODPjbVnZVyEaO8XoRWMmXz0pchlGlOw6zKa3VDQRsRwMSHYNF3pWF0+VBzNPnIlVc1YVJNdN9H6iz48v4EOPP3YoTre/G9Wu6qy3n24YWrqeSqnKm0udlPr3cDKiqsUuQ97V19frpk2b8rpNuaV0pvwBAL1p4J03IiIqPBHZrKr1xS5HORKR4wHcDeA4AApgpar+Otn6hbgfyYW3y4uxy8fGNnrECYfdEXMjnOwG1krjuqWjBTPumREJYgDAsIph2HDFhoSnvmblcTvc2HXdrpJowPZn+aLrdsPbG/LWqCj13i7lIN/XQfQ52X94f0yuj2S9LpLtH0DasjVta4rpLaVBhdvlTvt5z0T0PvK53Xiprud8XevGdg50H8CcNXNiks5W2ivxzJXPJO3Bkm0Zcjmu6NcBZBwsNXLSpLqGCvU9kuv9CHtkEBEREVnjB/A9Vd0iIkMBbBaRp1R1e7ELZuVG0+zprdvlxv2X3B/Jgp8uGVy6m9hMns4WKmdAvvRX+aKfhvb6exFEEL6ALy/5BkqlV4OZUgiyWClDvhOyGudkyboluLXlyOwrM06Yged2P5dRr4spo6ZY7i2VTWPXqv4aChR/PRvnb8u+LVj65NK8BP+MfXi7vKGea1FS9WDJtldDqveZvTbpuEkxwS+jvJnsP3rdHn8PbGKLed1us2Pdm+sw66RZeQ2s5ht7ZFjEHhlERDQQsEdG/ojIWgC3qupTZq/3V48Mqzew/dXDwOrTWfbIMN9HvGQ9WspZJtNc9lcZlp2/DHUj6yw9+Y7vSZFqXbNttXnbMOG2CSnLl0mvi1IICuVL/LEk63FgNLAdNkfMrB9A/j6nZt9lZtep2ee40l6JtZevjZllJP64zHJ/pDrvdrEjoEeGAy6euhgrLlyR0XeVle8cABjqGgp/0A9/wB8zi0k+vwPZI4OIiIion4lIDYBaAC/FLV8IYCEAjBkzpuDlyCQRYb6fLCeT7uls9A19f5QnW/1RX+mSMQL5T2pYbMmmuRQVOOyOyEwJhXzqa1aGq/9ydaTxZhZ8S9aTwmhMGqwEFps7mtOWMZNeF6Xc8wawHmiJr7uG2gY0tjbCZXfhsO8wxCZwO9wxPZfM5DvZaHTwZOzysTFJe5P1mukJ9ODi+y5GUIMxxxF9XKZT4obLDiBhm9FBDAC4tflWXH7a5djxwQ44bLHN+mR10H6wPTQwMopDHLDb7HDanej0dQJAQnAo3XaLgT0yLGKPDCIiGgjYIyN3IlIN4P8A/FRVH0q2Xn/0yMgkJ4WhmE9vrTyJL7Wny4UsT655S/JdlnyM70+3nZaOFnzuzs/F5B6I5xQnOr7XYakc2ZTb7HMTLdlT52Q9KbYv2h7JcWHlyfjzu5/H2XeenbKMLrsLe5fu7ddeF5nkY7Bahlx6jGXL7XBj88LN6PR15i1fhbfLi9HLRidMo7x36V4ASHgtW8a1DyT2yDDjsrtQYa+w3Csl2TX82NzH0NLRgl+9+KtIMMMMe2QQERERlSERcQJ4EMC9qYIY/SWbGQOK9fQ2We+RXdftigRd8pk9P18N82zrK5f8C8UeZpHt+P6GugY0bmlMuh1vlxc7D+xMGcQAgD7tQ+u+1rSzrGRb7nSzeSR76pysJ0VzRzPGe8ZbzqvisrsiZU4mEIx9+l7oz226usymrjPpMWald1I6Ro+ahtoGTF45Oa/5Klr3tSacL1/Ah9Z9ragdWQsN5uchs9hCD889VR401DXg1uZbU67vC/hMr6OGugbT66XT1wm3wx1Tzy67C1/501fgsDlw2H84Zn2X3QUbbHA5XCXXa86WfhUiIiIiEhEB0AigTVV/VezyAEcawm6HG8MqhsHtcJfUjWY0o6ESLbobdXSj51DvIXT7u9GwtiGUdC9DTa82YezysZhxzwyMXT4WTduaLL9vzLIxmLZ6GsYsG2P5fbnsf+7Eudh13S5suGIDdl23C3MnzoWnyoMpo6b0y3nMtt7N3ndr861Jt2PUyYJHF1gq18Geg2jpaElajlyul+jPzVDX0ITXkwUDp46aaro9Y7nVwGLN8Jq0Dd+ABtC6rzXlOvmSri6Tvb5+x/qU9Z3uMx8tXXDJTKWjMvK9d/tFt+Ov3/grNi/cjMbWxrxcz1avp/aD7RjiGpJR2dNtz9vlxW9bfpv1Nhq3NJqW3ey69gV88AV9CUEMAFg4eSF2L90d8/1UKhjIICIiIrLmLABXAPi8iGwN/5tV7EKZNYRLUc3wGhz2xd4od/u6Y5L4WW30pJJLw3zew/PQE+hBV18XegI9mPfQvIwDKdnsvz8DF/GyrXez98UzthNdJ119XZbKNW/tvJSBoFyvF+Nz89dv/BW3X3S7pWDgeM94zDwhtpfIzBNmRhJ+Gk/RoyV7Mm48eU/lYM/BpK95u7wpAz2ZSFeXZq93+7tx8X0XpwzUZdJjzCwou3jqYrgdblQ5qxLWr7RXYu1layPfexefejEAYM+hPabH0rqvNWl9ebu8WPfmuqR5JgCgdmQtnOKMfV2cqB1ZaykI47Q5E45riCMx+GH0sNi4c2NCToxMJPssxNezy5b6M3z7ptsBoGjfT6kwkEFERERkgao+p6qiqp9U1Unhf+uKXS4gs4ZwPhtAmYpvvEX/ns0wGTNWGrhmddC6rzUmOz9wZIhDvvcfL1/nJJvtZFvvVhpvxnaSBT2qnFWosFckNBABoMffkzIQlK/rBQAuPvXitMFAb5cX63esxzO7nolZ/uzuZ2N6LjRuaYx53ezJePvBdtjFnrZcwyuHmy7PtseRmTZvG17c+yJ6+mKTTkbXZbJz3dXXlTJQZzSaK+2VqHJWodJembLHWHxQdsWFK7B54Wb853n/iQpbRcy6IoLakbWYMmpKJAnnjHtmYM6f5pgGTOf8aY5pfRl1ueTxJQl5JqLrwFPlwVVTrop5/Yozroh8ro3ggNvhNj22jfM2JhzX3NPNg847PtiBHR/sMH0tXoW9AkOciQGR6CBxvOh6/t9Z/5ty+/6gH2tfX1u0vxmpMJBBRERENEjkswGUqfaD7Qk3+ZWOykhDIF/DZNI1cAtdB5k2sPNVnmy3k229p3qCHr8dszqptFfioUsfQutVrWl7J5gFgnK9XuLra8PODUmDgca6F993ccpZJqwGsapd1ZaSONaOrE1Yns8hWEvWLcGE2ybg2098G73BXthgM63L6Lo26x2RMlCnoaADcOT/qQJu0UHZplebMHnlZNzw1xsQRBBOccaUDwDW71ifUB9iE1TaQ8NOKu2VEJuY1ld0XUYHMYY4hyTUgVmQatXWVTjv7vMwdvlYvLDnBagqbGKDLa6JvXjqYpw15qyE4/rjK380rbITjz4RXb70PZdmnjATAkmYhQQw7/ETXe9GPY85Kv3sWovWLSrK34x0mOyTiIiIaBBIlnhv0nGTkmb3j05YCSCnBJRWGvjppm61ItW0qamSD9aOrE1IwOiyu3D8UcejpaPFch1kMm1rJskQzRjnp9pVndN2sq13s/fdeM6NCdsxq5Nl5y/DCPcIAIA/4E+5n2SBoGzLnUm9R6+brmxWg1idvk647e6YpKfGFJgOmyMytafZ8SQLGGQ6JWabty1mGlkACCKI3130O0wbNy1hW0Zdt+5rxew1s2MCOsnOj1ndzX9kPmywocJRkXYWk/lr58fUp9PmxP2X3I/akbUxU6HGn5tKRyXuv+R+jHCPwIHuA7j0gUtjt5NiilMglGhVEBsISJaM1AiARJJyhkeDVNor8d8z/hvTT5geGXqU7Liine45HTPumZGwf8PvLvodnHYnTjz6RMy4Z0ZCYC26DqKviWSJTI8/6njT90frC/ZFZvjJ5Hul0BjIICIiIhoEkt2I195RG9N4MhoV0Te+3f5uaFAxxDUk69lErDbw8zE7Q7IGbqpZJaaMmoK75tyFhrUNsIkNQQ1GZj+wiQ0+vw+QUMMnEAxg1ZxVCXVgBBamj5uOXdftStvAtjrLhZno89Pj74FNYp8Cp9tOvmZniX9fsu1En5Mt+7Zg6ZNLQ8fe140gggnrV9grUOGoSDtTQjblzqTek31uqpxVCQEH4xpf8MgC2G12BIIB07LXDK9BfDvVaXdamjLUrDdHt78b1a5qS8dunPcX975o+vpr3tcwbdw009c8VR7MPHEmVs1ZlfA5BhAJ+KX6vBkNeKMBnqxhbDZLSF/wyNCvdMGl2pG1keBlquCSWUChN9ALIBR0McqWaTJSl8OFM0efGRPESHZc0V71vpr0Nac4cdaYs9Dp68SB7gMp9x99jKkCd52+TthhRwDW8nFY/X7qDwxkEBEREZWBbKcTNZjdiBs3tfE37gASbnwB5PxULh89Lqwya+Ame2Je7apGS0dLTACi2lWNSXdMil1fjzSm5j00L6YOspmeMts8D2YNk3jphrPEl7U/zoux3c/d9bmUZQeAu+fcjXEjxuVcHrPPTc3wmoR99/h7TOvL7By5HW48dOlDkcZyDJOhFPGSBfXiG71mZTfrzVFpr0SnrzPl+4DY82585uP9zwv/g/954X9SXr/xn2Ojd0T8tW8pCWZcw9goe6pEp9kEl5IFUI3XBJIwa4cxverME2fGbMdhcyTk04jn8/vw4t4XUe2qxnjPeEvHlYzL5oJNbPjm5G9GppXt6etBb9D8HAKxSWZTBe58AZ/lIAaQfR6aQmAgg4iIiKjEZdNIjhd/Q9/r701ozBg37iPcI0wbCgZjFoAR7hEZNzTz0eMiW2aNGqPXRXzdrt+xPmUjzEgEOvPEmVkPETFmuYh0S0fyWS6itR9sTxgXbwxNiO7FACQ+JTcr67yH5sFhd+R0fVmVrBEazWV34fTjTo9pnGcj1ecmEIxtvMX/bjAdFnPBkWEx0cyGUiS7DtIF9ZKVvWZ4TULDM4hgTA4Ys/eZnXeHzQF/MHZYj/FadG+EZPWSbriWWd35A/6YpLrx+Wuiy55Mtas6ac4Vs+DS3IlzMem4SWjuaMbUUVNjAkbGeVizbQ2+/cS3E/ZlTANcM7wGcyfORTAYxH2v3Yeuvi78dedfI+sdP/R47PloT+T3nkBPZHszT5iJZ3c/GzkuG2ymvZCSEQiCwSBWbl4JX8CXNr8KEEoye+M5NybtTWLU+7o3M8tXbeX7qb8wkEFERERUwnLNoxAtuvG088BOXPbgZabrpXuS2uPvwew1s9OOc48+hv7ohWFFdB1Uu6oxeeVk07rNRLZDRJLNcmE0QJKpdlXHPJEHAL/68fQ3nobL7jJ9Sr7s/GWoG1mHA90HEsrap33o8/dldX21edtMG4jJjvdA9wH0+mMDaPENu8+N/ZxpcCnZNs2urWSfm2Mqj8HBnoMJU1sGNBAJTMWLbgh7u7xY+sRS07KZXQd2mx3r3lyHWSfNsjyMKtVnHgA0GBvFMn5P9r4xw8Zg/VvrYbfFzpQyxDkEv7vod2h+pxm//PsvY16L7o2QSrprP6H3xs4NlvPXmPnK/V9BIBhAQ10DGrc0xmwnWVmbXm3CgrVHhvssv3A56j5WFzOs6vKJl+N7T34vJshigw3z186PnOtj3Mdg70d7TfcRHcSIt/7t9QCOBIlcdhc0oFBR+DV1fhgAR3peZDAba/Q5SNUrZeqoqdY3CmvfT/2FgYwyJbekn3s6mt5kks6WiIiISl4ueRTMGDe2NcNr4BRnzI27U5yRJ5rRN749/h5oUOF2uSNPVXu0J+04dyA/vUnyzaiDlo6WhJ4Nqor2g+2oHVmbUD/RomeVyHaISLbnttPXCZfNBV8wNjGpy+7ClFFTTBuFV//lagx1DYU/6E+bXNPq9bVk3ZKYhJGLpy7GigtXmK4bfR0YM1C4XW74/L7QcJ2o8/DU208BQNrASqpry6xuu/3duPi+i9EXMD+nb7z/hukTfWM/0UMKzBLmmvUU6PR1YsnjS3DNX66xfO2nui4AwGF3oM9/5BgcdkfS5JWBYABn33m26X76An04/bjT8W7Xu2nLlIyVaz86YJNJ/hozRi+dxi2NeOqKp7Djgx2mQbToRLjzHp4X+hyHAwFX//nIZ8E4J54qD1ZfvBrzH5oPSCg4JHaJ+QwlC2JkKhAMhHrVFLB5ZjWR8njPeCyeujimV1gqpZQjg9OvEhEREZWwbBvJ6Rg37pX2ysi/1Revjmlw7LpuFzZcsQF7l+5Fx/c6sOGKDXjkskcwxDUkZlvJpl/M51SRhWDWs6En0INqV3VM/VQ5q+AQB5w2J6qcVXA73Lhrzl0AQkM3ACRMBbrs/GVoP9ie8lizObfeLi92HtgZE8QAQk/QjYSPZtOAAqEZFsymp4xf18r1ZTbrxa3Nt6LN22Za5ujrwBfwwWF34P5L7sfqOasTekfEc9gcWPfmupi6THdtJetV1NXXlVB3hhv+ekPCFJPJpug0qCpq76jFjHtmYPLKyWioa4Db4Y5JvmnUe/y1n2wa0lTXRbJknzsP7DQNpJgda7WrGm6HOzKs6kd/+1HCOkZQM1qbtw2rt66OOcdG0NOYmjWf0yanc97d5+Hadddi8srJMecsemrdSbdPMg1Gmp2TF3a/AJ/64Av60Ie+hJ4v+ZLues8HsyEgniqP6RTDnxn9GVTYKmBHbI8dM6WUI6NggQwROV5ENorIdhF5TUS+E15+tIg8JSJvhv8/IrxcROQ3IrJDRF4Rkbqobc0Lr/+miMwrVJmJiIiISk10Q8FoJGfTUDAVvk+P73IevW/jxtf4uXZkLQ77YpPidfu6TW9uzRrUyYIeViRr+GWr09cJt8Mds8ztcEee+s6dOBe7l+7Gxnkb8c733sHLV7+M/531v9i8cDOgiDSWxi4fCwCRwM+y85dh6ZNLI69FN7KiZXpujQbagkcXJLwWnfAxXaOw0lGJtZevxYYrNmD30t24a85dGV9fzR3NaZcb56t1X2vCdeCwObCvc1/KfRg+8n2EJY8vidRlm7cNtzbfmnDdRl9bnioPGmobLG3f0NXXldC4TRYUMvQEetAb6I0EUxq3NGLzws249cJbUemoTFjfKF90Yzv+Gkl1Xew5ZD6E4cpHr4wJpAyrGJa03N8987vYvHAzGlsbkwZorppyFQBEPm9L1i3BhNsmYP7a+Zhw2wQseXzJkZU1FNABjvw/WvTnNv6479h0R9Jg4MwTkg9r6fZ3ozfQGzln8x6eh/U71qPN2xYbNEsStDIY14xZYC5Zb6xMJZtKNVe2FE35xi2Nlr4njUBdb7A3adLPgvztyQMxu9jysmGRkQBGquoWERkKYDOAOQDmA/hAVf9LRH4IYISq/kBEZgFYAmAWgE8B+LWqfkpEjgawCUA9Qn9uNwOYrKpJ55ypr6/XTZs25fd4MhzKUWo4tISIiABARDaran2xyzEY5Pt+JN95JrxdXoxdPjbm6a7b4cau63ZFtm+W/8Db5cXoZaNjGsouuwt7l+5NKJeVfVhViCEqmZQvZsYHfy+CCMbUgfE+ABkfs5Vza1bWaPH7aNrWlHSGBbPyZJLrwlh/wm0TEpZvX7Qd4z3j09YXgEj3fl/AF/OUWiDQJP3uHeJImlcg+ri8XV6M+uWorBqjDnHghYYXIsN0UtV7fGLKYRXDsOGKDah2VSetn2OHHGvpGjG7LtbvWI/z7z0/adndDndkyIXD5sDXH/56wjrPXfkcXHYXZtwzIzITUTynOCMJYHv9vZFhZJkeSybXQfzsORve3oArH7kSIoLeQG/Sa8IwxDEEAQ3AJjZLCTGjy5os2afT5oTbGRpSd6z72JS5MHLhgAMBBNIeo1XGdThl1JSU67V0tKS8DgDgsbmP4f3D71v+brAq1/uRguXIUNV9APaFf/5IRNoAjAIwG8C54dVWA3gawA/Cy+/WUGTlRREZHg6GnAvgKVX9AABE5CkAFwAwD20TERERDUD5nu0jXWLCn/zfT2KeUC6YtABX11+NA90H4Ha4YxojlY5K03HT6aY+tCqfCU8zLZ+3y4vWfa1pExFG9wbINO+FlXObLodAfFfy6DHxW/65BUufWJr0GLMJEpmNrV88dXFkqsn4+jJ7emwEWFx2F5xwhpIxagCBQCBpAMIsiFHtqkYgGIg5rtZ9rSmDGMZMEjaxoauvK2EfxvVt9OyIf1pviG+UG9O4th9sTzpNaqev09I1En1dGEGN4486PmXuFlXFeXefh0pHJXr8icEHAOjydeHkkSennZUnOgGsmeaOZkzwTEjI9RDUINa9uQ5TR021lMDTuA4a1jZg13W7YvK8pJpiNF789KmpRAdPPFUeHFd1nOl6t826DWd87IxIMOnP//gz7tp6Fx5se9DyvqzwI33Sz0xYHQJiZUjPV+77SuQ7vxRyHBn6JdmniNQAqAXwEoDjwkEOAPgnAOOqGQUgOsS1N7ws2XIiIiIiypLZDayRmPDqv1yd0AhatXUV/vTanxAIBhKmDkx10xyfZA5InBI0mtlT6FwTnqbq8ZBqGkyjgW/lCW90HRQip0m6Bkeq2QQuPvViXHzqxRnN7mElSLTiwhVYVL8ooSeH2flKNd1kpaMS919yP0a4R+BA9wFc+sClKZ8Qx7vstMvwvU9/L6Onxc/ufhabF27Gmm1r8JNnfpLw+o4PduCsMWeFZpZpbTTZgjkNKvYf3o8D3QcQP6JARFDtqsaeQ3syukbiA01XTbkKv9/8e9jEltB4N3pOxE+tHC86iGe32bOa6taY8SI+z0xvoBfXrrsW/qA/5fCHeNGfaavJP7NR7arGigtXxMwkM23cNNN1Z586O+ZzMKJyhOmQISAUkLOJLWkAyZAqEJUrl7hgt9stB4yjrwOzoB4Q+j4zrtd8BJDzpeDJPkWkGsCDAK5T1Q+jXwv3vshL/xkRWSgim0Rkk9dbGgmkiIiIiEpV9Dj8+MSESZ/k9nWhJxCewSSDcdNGfg1jStBkuSOaXm3CmGVjMG31NIxZNibyei4JT1PlIogvX7IGvtnNfTyjR0ShcprEJ1WMF597JP64H2p7yHS7ueYxGe8Zj3mT5sUEETJN3NgX6EPtyNqkOVjS+dNrf0pI+Hj8UcenfV+nrxOXT7zc9DWjkZ5pPpeABlB7Ry0ufeBS+AN+uOyuyHXQUBdKrnnpA5cmTEFrlpwRME9q2rilEVuu2oKn5z+N279we+Raq7BXJOR8iRedxNNI6Hvrhbei0m7eODfE53mYecJMjPeMN80zA4S+K3oDvQlBjlSiP9PZJP+0KhAMmE6H67A5Uv4+856ZOPvOs3Hvq/eabveeOfdg7WVr4bQ5U+6/UEEMAPjqxK9i13W7THtNJMsxZFwHG+dtxIJJsfl38pnjKN8KGsgQESdCQYx7VdX49nw3PGTEyKPxXnh5B4Dob5zR4WXJlsdQ1ZWqWq+q9R5P8SNERERERKUo+mY2uiEz1DXU8jbsNjseuewRbLhiQ+SmOV0iznSzTHi7vJj38Dz0BHoiAZN5D82Dt8ubdXAg2T7bvG1JyxqdoDKTx23RyfWiZ3xJ1qjIhrHdhy59KKHxGN0INDvuq/9yNc67+7yEYE4hZsWJP1+V9sqERiFwZPaM+HMptuS56WyS2Hzp9HUmXE+dvk647ckb9d3+blS7qiNDZKIZQ2SMMmbSKyCIYCT5Z5/2QVTwH9P+A09d8RQatzRGzkl8D5VkyRmTBZo6fZ2YMmoKrqq/KnKttV7VmvB+l90Fl7hQaa9Eha0iZmYiw8jqkRCJrXOnOGPOn9Me2zh/dvez8HZ5014nTol9n1kC0ujZToAjMwE11MUma7WLHcMqhiVsMx2X3RWZpcfsevN2ebFm2xr4g7HDO/xBPzbu3IiWjhb8+R9/jkwLnOw4Tz/udIxwj8CKWStQ6aiE2+FGpaMSi6cujgQgK+wVcNmSJ4/N1VnHn5V0Gux0AV0zgWBsAtBSmrWkYENLJPRpaATQpqq/inrpUQDzAPxX+P9ro5YvFpE1CCX7PKSq+0TkSQA/M2Y3ATATwA2FKjcRERHRQNX0ahMWrF0QykUQDGDVnFWYO3EuZp00C9f85ZqYdR02R8KNvaEn0IPjjzo+0tizkmMh2VM8oyu5WU6DPu1D675WzDxxZsohIMkk655ee0ctKh2VCWWNSUwYME9wmEz8UJf4vBf5StbqqfJg5okzU+b2SHbc0bkIjO7h+cpjEi/6fFW7qnHabafFvC4QPPjVB1E7sjZmX+0H22GX5NNABjWI5658DuvfWo9fvfirmCER0eegZnhNwtCOaNGzvCQbIgMcCYhk0rMgWm+wF9c/dT2CGkxZHlU1HSplJdAUfa3Fn8uG2gY0tjbCJrZQGaJEX+9G75FKR2XkGph03CQ0dzTD7XBj4Z8XxpTDqOspo6ZE9mmWWDb+Mx1/LG6HGw9d+hBqR9ZGemwZiUHj86EIQkGhtw68hV+/9OvklRln4eSFuPGcG1MOH0vmioevgNvpTjr0xiEOOGwOfHPyNzF55WTYxAZfwAdVRYWjIhIIUNXQ+VfzKXHz5a0Db0V+Nr5zql3VCcPHFjyyAMdUHhOpd2MIXXzvs4AGQlM0O1x5+27Il0LmyDgLwBUAXhWRreFl/45QAOM+EWkAsAvApeHX1iE0Y8kOAIcBXAkAqvqBiPw/AC3h9X5iJP4kIiIiGixybQgbPR76tA/GLHvzHpoXadBGj5MOajDSkFmzbQ1+9uzPYhoVTnFGbuyt5lgwe7Ld7e+GL+BDS0cLDvYcTHsMmSY8NWsEGmUwcggYZQVgKTFhMn2BPvgCPqzeujqhMVyIGVfmTpwbaWga+4tuuKTqlh8fdMkmSJRM/HXqqfLgvm33JczGoFBsemdTZJiD8T5fwJe2/tu8bTjr+LPwPy/8T8zy6Aa+cU0veGQBRCRhmyISEwwY7xlvmmMjWUBkiHMIAsFA2lwUACwFxHoCPTFDvIAjdbLsgmW47vHrIgHI6J4LxjEYdR4fQJq8cnLMsTesbcAxlcfg+KOOT7jeK7QC/zHtPzD9hOnY+s+tkYa5kYA1mi/gw4t7X0S1qzohsaxR1r5AH+w2e8z+42edaahrwMwTZ5p+j8Tzqx/Xb7g+oZdAOo1bGnH5aZdjxwc7InUc38BPxhf0wdeb/LP039P/GxecdAEm3TEp4TPn7wsdZyQhbmbFzspRrqMAxH7n9Ph7Enoy9QR6cPF9FyOoQfgD/pTDXb575ncxZ/ycvM2YlS+FnLXkOSSPO55nsr4CuDbJtlYBWJW/0hERERGVj3w0hNP1eIAeeWoYui0LNe4un3h5QjLEPu2LaRDED8GIf7rs7fKiuaMZFfaKmIafQxyRGRZ8AR/sYo+ZhtNld0UautmI723Q6++FDbaYp+vZjvl22V2wwQa7zY6gBnH2mLNx9p1nR15fPHUxVly4Iu8zrhiN2y37tuC6J440br9Z9000tjZGrpGGugY0bmk0f0pu0j0821lxogMX0U92gxrEsvOXoW5kHXYc2GH63lueuQX/8ex/RHoNuOwudPelDyJd85drUOWqSp9nQkMBC5vYQnkLFHC73JEny0DqxLNAbEDEqOvlFy5H3cfqUDO8Bp9f/Xls826LrH/80OOxv3s/BJLRLBpuhxuvvvtqJDC19Z9bI5/5bn83NKiw2+wQEbyw5wU0rA29dth3GGKThFklPFUetHS0JPTM6fZ34+L7LoY/6EcwGNtDozfYixv+egOuf+p6+NUf0yPLYXOg0hZ6Mn/Ydxg9/p7IdKXGte6p8mDHBzsiw1SMIEi0+F4WRpJaq8k90yXSNNPr7435bNrFjmpXdaiBn2OmhYO9B/H87ucLls8jU3ab3VJQCICl3D8AUOWqSjuNazH0y6wlRERERJQdKw1hK701kvV42ObdhjfefwPXb7g+9OQ43O4w9rHn0B7T9+05tAfjPeNDPS3iut1HP102hrPYxJbw9NqvfvgD/sjy6Gk4jV4h+R7iMHnl5JjXjUb9/sP70zaiHDYHhjiHRLrs/37L7wEAwWAwYfz8rc23YlH9oqRd0lPNuJLsfBoBrZjgRPh8GdODGsfQuKURmxduRqevM+30q9mKGYrj74U/6I9pqF79l6sx1DUUPr95I89o/MWXPR2/+k1nNYmeuSX6c2OodFTG9DgwhjGkDQ6GAyJA6P8f9XyE7d7teLfr3ZggBgDs+WgPHpv7GJ5ufxq//PsvLR0PELoOL3vwssjvxtCu6PL39YUCkcYT/shrAZjOKpEsYWaqBmyy1/xBP+79SijJZXQ5jfIsql+EY4ccm1Dn8YGC+GClQiNTtWaa5NWq+HwkAQ1kNCtOKmYz3hST2+E2DQpV2iuhUDhsDssBDMPnaj6X72LmBQMZRERERCUsXY8Hq701hlcON93+99Z/L+m+W/e14o3330hZPmPWguibZrcjNKY8ZjhLlGpXNfoCfQnTmkZPw2kWlIlv4FsdbpMqh0Dj7EbsP7zf2hSbCvzHtP9A3cg6zLhnRkzgx4zxdN1sSM3OAztNy53sfJo1zFMWVTWSEHLKqCk4Z8w5pjkgsmX1qa8RcLHBlnIK1nzZuHMjuv3dcDvcCY25Hn8PbvjrDfjBhh9EutOn6yVjVu//tuHfUpbh4j9dnHSKzngV9gooNHFYQpL8NOlEDxuyMrVmJj7s/RB7PjQPbG54ewOGVQxLSOpqFkSI1uPvwbXrrkUgGICKxnzXmeX1oNTePPAmLjjpgoTrSUSw4YoN2LJvSyhoHdWzJT64FO+x1x/DWWPOKliZs8VABhEREVEJS9XjIZNhC5kmmOv2d+NLTV+Cy5GYYT96Csea4TXwB+Ky/Qf8qBleYzqcBQB+9vmfYfoJ0017R0Qnf2zztpl2tfcFfDHDETIZbhOfD+In//cT/OuD/2qpTvzqxw//+kMEggFLXdLdDjf2HNpjmixywaMLIr1OonuMJDufVrveG+J7xUQP+chHjo5My1NdUY3fXfQ7NHc045cvWu+pkIluf3dMbwGzc5SsMZ8s2WamxwkAfcE+9PmsTbFpJcdGJuKHDRnXe+u+VlzUdBH6gtlP/Xntumtht5knYr3+qevhtDsThjFZkeycZBvEGMwBkDtb78RdW+/CZ8d8FuvfXh9ZfvaYszHjnhlw2V0IBAKRxK4+vy9t0OzhNx7G+H8Zn7cgaL4wkEFERERUwjp9nXDZXDGBCJfdhU5fZ+i1uEZWfCJHw9PtT2e8795gL3p9RxpabocbqopVc1Zh/+H9WPfmOpx49Imh6TKjH+hJqDdHsuEspxxzCsZ7xqOhtiEyrACIzXGwZN2SmNfiu9rHD0fIZrjNP97/R8w+rDjcZ979Pf6ppg02LHh0QShRoklyQqPxNu+hebDb7EeSI8bN2GGcz2TDBJKJ7hUzf+38mPfOf2Q+Jh03CZ2+zqwT+NUMr8mscR/ow7Rx0zBt3DTTQEaFrQIOuwP+gB8qiT0UohkzRaRLohlEEBW2CthstrRlNUu2CZgnjM2Fy+aCXewZz4Jigw3VFdXo9fciGAwmBAiN6XiNHkbGcI3xnvHwVHlCgccMphM24wv6YNapRiDoCfTEnI8hziEIBoMIItjv+SOCGkSFvQIOmwNBDSY06qOlmp2pHBnnIP54jaFv8Yld3Q43vvXnb6Xc5j8++Afmr50P4Eg+lFKQW3YTIiIiIiqoald1Qm8KX8AHX8CHA90HEhIemiVyBIBTjzk1p3IMdQ3Fb7/wW+xeuhsv7H4BE26bgPlr58ck0YuUIdiHi++7GPMemZeQLd9hc6B2ZC28Xd6E4RyNWxrh7fKizduWEGBI19iITtrZ9GoTxi4fixn3zMDY5WPRtK0psp7x2rTV0zBt9bRMqiB2f+KE0+ZEhb0CFfYK3HPxPdi+aDt+c8Fv4BAHggiiq68LPf4eqCgq7ZWoclYlbKdP+9AT6EFXXxd8QV9CA9c4n8YwAbfDDZctsZeMGV/Ah58///OEhqQv4MOk2ydh2uppGLNsTEz9ZEKD6VvGQxxD4Ha4I3k59h/en3J9m82Gc8eem3Ydm81aM8Yf9ENSzXsaZhe7aT4To94rHZWW691KmdKJL3OFowL3X3I/Wq9qTbq+quKu1rsin80Jt03AkseXAAj1LBniGmK5jJX20PFWOavgFGfKdeNnpAEQSSR68tEnW95nPkVPezq/dn7ks1lpjx3yIxpKlDqsYlgoR484MaxiWOj47fk536XISOx67TrTuTaSurX5VrR52wpUqsywRwYRERFRCUuWbNOY7SOIIJzijJmNwewJ++xTZ6d98gaEAhb+oD9hSj5/0I+R1SNNezFkklDQaOCkyv2x3bs9bTnjGQ3+VMNtACT0TkjF7XAjEAyYPkHv076Y8t/VeheevOJJ7Dm0J2FmBiNR4vDK4Zjzpzlpewe4bC5UOisTz2d4Zpn4XhuGSkeo8dUX6MNnx3zWNMhk8AV9kQBZ9DS8VhkN43RJEwMaiGmUN3c0m67XG+xFbzAUlEv29DxS9nAgzzDEMQQ9/h7THBwBBGJmD6l2VcPX54NPY6+BgAbw8j9fNu2h8sLuF7KaLcO07BaHeMUHB5x2J0a4R+DYIccm9oACIscYX3dGIs6a4TUZJdMUEWy9ais6fZ3YeWBnQoLPdIyeAfHJUFMxhonlg1/9kelPG9Y2YNd1u3Dm6DNR4aiI6Tnidrnx/TO/j037NuHS0y5F3cg6NHc0w+1wY+GfF5bMbCSFkG3OlOaO5pIYYsJABhEREVEZ6g30RsbXux1u3H/J/TH5JeJ5qjyYccKMhNk14v308z/F5RMvx4adGyJJMXv8Pejzh3pZxPcAMbjsLjhtzrQ3x0EEsWbbGtSNrEua+2PqqKkptwEAM0+YiWd3P5swE0dLR0vCusbMCMYUlVYFNICtV29Fc0czrv7L1Skbs+vfXo/ndz+f9PXhlcMx88SZMclGe/p6TBu293z5HowbMS6mUW0EaJINp1g8dXFkGktfwJcyiBEvZhpei6wOuTCuUSOYdOLRJ1reh1VBDcJpc0YCIckMdQ3FigtX4MPeDyNTh0b79hPfxref+HZMDhGz3kHFYATq2g+2Z3wdb3h7Ay6feLlpACRehb0CAkHj7MZIY7VmeE3ahJD5UKiggdPuROu+UE+WLl/s99OHvR/ixv+7EQDw6D8eBQAcVXEUfAFf3oJX/ckudrjsrsj0y8a006lysVTaKxEIBhDQgKWEvFa+n/sDAxlEREREJax2ZG3aJ5UOmwP7OvehFrVJ12nztqUNYkSLThL4hT9+AX4cecJp5sFLH4TL5sLsNbPT5i74wYYfhBqf4ozp9WHkdZgyagoWT10cmWbSOMboLvnP7n42Mr1odIO/2lWd0OPBmBmhL5BZokMNKo4dcixmnTTL0vCEe1+5F0s+tcT0teOPOh5AbLLRZAGH0487PdKINHJ9HOg+YJp00mlzwgYbPnP8ZyIzVax4yXwMu13sEBX4kXgeD/YcREtHi+WcGdEzYqQKyhj6AqFgyb7OfXDAYVqGbKW73gy9gV7MOmlW0uEt0T14jBwij7Q9Yrpuhb0CABJm3skXG2yocFSYTpmbaYPfmJLTSo4Mu80O1dCKRrLdE48+ETbYEIiLglS7qtHn70sbQCq2Hn8PZq+ZDafdmdBbyky+pmYths+M+gwevPxBeKo8kcBmuh41voDP8oxCi6cuLoneGAADGUREREQlzVPlwV1z7opMoRgIBhIS6H3k+whLHl+Ca/5yTdIZKTa8vcHS/n70tx/hBxt+ELOddDf/TnHiuKrjMGXUFKyasypt4zZVw8/I77HiwhVYVL8o0s17/tr5CbkF9hzagxHuETHLOn2dprOEZNONOqABtO5rxQj3CMydOBertq5Kuf4I94ikQ4H2HNoTaQAYAYeWjpaEslbaKyO5GuKnYo2fHQZAZBaK6OEhRuLHeA6bAyICDWjM03WHzYF5j8xDhaMi6xlg0jWW/OrHF9d8MRSQyiGIIRDTnAxWGDk9jh1ybNrpYGvvqEWlozJpctfPHP8ZfPfT38Wl91+aVVnScdgdeOqKp7Djgx0xs0V4qjz4xIhPZDRkY8xRY0LDcSwMazGO9xsPfSPmcx+fJ8Po3TKyeiTmrJmT8Hmrclahu6+7X6bcNeOAAxXOilBPg2AAPdpjOeBVzo5yHxUJeBnfMzXDaxKCxtHSnSOb2CLTBH/m+M/kvczZYiCDiIiIqMTFTxlqDPtw2ByR6Q6N/yebfvW4quMs7St+O1Y47I5IAGLuxLmYdNwkNHc045ghx+CLTV9M+j63wx2aYcBREXnqDCDSM2C8ZzzGe8ajzduWEPwwpod12B0IBANYNWcV5k6cGypH+s4TlgQRxEVNF8EudkuNoK9/8utJAxnRPR4ARKZbjS+riCTN9eGyu1CJStjEFpP3AYgdHjKsYphpGYzu5Q6JbQKoKnqCRxp6ya4hIHE2GKuNJSAxt4Ud9oSn/OlkG8QAQkMMbvjrDThz1JkYWjE06ZN3o75Tdcff2L4RG9s3xgxx6vX35q+Hhh7JgxMdXHp+9/MZBTGMqZLXvbkuo93HBy/jz6s/6Mesk2aFfom7hisdlfjP8/4Tez/ci/9+4b8z2m/ehHPBKhQOmyPj3ljlaurHE4d9eKo8WH3xaix4ZIHpd0c6QQ2mnd67GBjIICIiIioDRoMROBLYWPfmOix5fEkk+AAkn3512rhpsIkNQbX2hNTYjtnQFofNAYc44HK4Erq9R/ci6PH3pG3c/vUbf408dd76z60Yu3xsZH9G4y1ZL4voBJHRPRKMIQ/RgZ5s9QX70IfE8sfnDJh5wkyM94zHsUOOTThmu9gjPR4O+w5DbBLJcxA9lj0+10f8UJJKRyXuv+R+vPH+G6Y5HgzDK4enPKb4Rmp87gOHzYF1b67DrJNmxVxH8T1EontuRDeWghq09PQ/0yBGOkdXHI0Pej9I+vph/2E0tjYmzJYTL/78pRratf7t9Xhs7mN4//D7OPHoEzHjnhnZFT6OUX/xOUbWv5U6EapA4LK7IkOhVs1ZBU+VJy95DRziQIUj1Msh+jPfOLsRCx5ZALvNDl/Ah0AggB9v/DG6+/I/5MYqf9A/oKZVtepToz9lutwIMK/Ztgb/9dx/WU46Gy/Z35di4PSrRERERGXIU+XBrJNmJdysJ5t+FQg9AbcqetrPu+bchUpHJdwONyodlbj7y3dj99Ld2HDFBuy6blekMRvdi+BQ7yH0BnpNgxhDXUPhdrjRUNuAGffMwHee+A7q7qjD/LXzI+/t9nejYW0DvF1eS70sjB4JQOimffPCzVhx4Qp8evSnY9Y7fujxlusglfjG/7O7n0Wbtw3tB9ux4gsrItOtVtorYYMNPYEeHOo9hD7tgy/gixxj45ZGbF64OaEuzZJp9gX6UDuyFpdPvDxhakiX3YXakaEcKUY+jmwZQ5Wip66NP7fR5yciPKuKSJ66xGQoVRAjE/HXrAYVz135HL4y/ium689ZMwfXrrsWM+6ZgYa6BrgdbgxxWp/q1Kr2g+2Y+YnUCVkVajot7njPeJzuOT2n/RvnNeH8amiZQkOBP+3Dod5DWTeWKTt2sUe+A+I1vdqEySsn45d//2VO5yXV35f+xh4ZRERERGUqPuGi2fAMAJFkkQ67A33+1F2shziHQFUTpv2EAjZbKEcHAOw/vB/bvdtR7aqOrNd+sD1he8bTYWP4yLILlqHuY3WodlVj8srJMUMn4hlP/6aMmpJRYkmj54BNbAm5MfZ8ZD70I1d9gb5IXgVfwIflFyxH3cg6HOg+gDlr5iTt2u60OyMJTqMlO7dGXUfnTQlqMOa1ZMNbhjiHIKhB09kYKh2VcNqcSYcqtR9sT+ghEv101tvlxbyH56XsfZMLl80VaSj3N5XUw1kCGohcZ7e33B5KqioCBxyw2W2ha8LvyylHQ7e/G9Wuahw75Ni06/rUF5mdZP4j8zF93HTsP7wfr3pfzXr/QLh3Urj+o4eeGQEuKyZ6JmY0NIasM3L6xM9eFR2EzIZd7KhyVaWc3rsYGMggIiIiskBEVgG4CMB7qjqxv/dvzCAQnfgPMMmf8faGyPCMbn83NKgY4hqCXn+vaUOqwlYBh92B3r5eiE0gkJjhJzHTfoYbR1c8dEVMj4TFUxdjxYUrTGcM8QV8eO7K5+Cyu1AzvAb7D++PJPA0m4UjWvTTv+jcGycefSI+f/fnY3osOG3OSD3lctOeLb/64Q/4I0MBlj65FI9c9giqXFUJQ2KipXrCGX9uoxsQqV5L5sunfhmjq0fj53//ecJrP5v2Mxw95OikQ5WS9RCpdlWjpaMFOw/sTBnEsIsddtjhV39WCSCNwFUmw6PyxR/0W57O1q/+mKE7toANX5zwRRxdcTRWbDKfTcaKClsFmjuaMbJ6JI6qOMryzBq+gC8yW0wu4ofXGNeF8ZrVz1upBDEq7BUpc6CUqy82fRE2sUVyBgEwDUJm4qzjz8IvZv7C8vdMf2Egg4iIiMiauwDcCuDu/t7xknVLcGvLkalIjcCBwcifYZYgEkg9nWAQQQQ0EJpJIoiEJ65mvSzih1Xc2nwrFtUvStoToMvXhbNOPCvhOGxxo5yd4oTD7oj0QFh2/rLI/je8vSEmP8MpR58S84TZH/Tj0gcuRY+/J2G7xdDt78bsNbMR1GDKHAsNdQ0pGwfRuVGA2GSb8YzXkg0tuffVe5Pu580Db+KWM27BNX+5Jma5EaxoP9iOf534r2jceiS/xGfHfBaTV06O5ENJRaGhngI56u8gRq6CCKasd6t6g71YtG4RghpEIJB5bpFjhhyT0/7jr9/O3k786bU/4dyac/s9aJgPAzGIARwJ+M17aF5kCuFqV3XGU/ZGe+XdVxJ6jJUCBjKIiIiILFDVZ0Skpr/32+Zti2n8A0cCB9E9M4DsnrxFdxePZjxxNetlYeaOzXdgose8o8ruQ7tNjyP+yfxVU67CjefciPaD7diybwuue+I62G12+AN+qCh8AV+kLPHd5BVq+Sl1f7EylKBxSyMW1S9Cp68z7RPP6GSbh32HAQk9DQ8EA/hm3Tfx+y2/h91mTzt8yEzdx+pMh7M01DVg8srJpolT178dSjxp5footwBEKXDCCZvNFkloa0yN6rA5UCEVgKRvkNtgQ+3IWqzZtiavZQsiiF/+/Zf45d9/mdftUn70aR8m3T4JbmcoqfBnx3w28nnNVCkEhs0wkEFERESUJyKyEMBCABgzZkxetrnh7Q1Jl8cHMsy6/2fLeBKfbP/xtu7bikPd5oGEbe9tw54P0+emaNzSiBvPuRE1w2twVuNZoaEK+Z3YoiRF59aIngkkmmlvGz3SgyYSJMqyvsZ7xqOlowXTx03H5oWbI0N4ZtwzoyyfuA8EfeiD2Sgcf9APm90WmZkkFRHBSx0v4cHtDxaghFTKfEEffL2hvwfZBjGA0PVm5Fzi0BIiIiKiAUhVVwJYCQD19fWpMwRadFzVcWmXRw83aKhtSOj5kA1j2EAwaO1J+rO7n8Xze543fW1F8wq4ne602/AH/Ni4cyOaO5oLljSy1BhBgvhpNo0Gg5EbxUpOkVxMu2saXI7wEBgFhriGhIbpSGk+jR3srAYsAxrAF5u+WODS0ED2Yd+HmLZ6GgLBQEzujWJjIIOIiIiohE0bN810+enHnY6WjhZs2bcFS59cGsnD4A/4TdfPVKZP8IIIJh0+EEQwYfYQM33ah8sevCyj/Q400TOBpMspkk992oe+viPBo1IbpkOlr9JemdPMLFS6jO/veQ/Niwm0FhMDGUREREQlbP/h/abL61bWxUyXye7/A4Mxi4mVnCL9wSEO2G12uOyuhBwZ1L/ssMMGW2jISQkqxtS41L/6tA+t+1ox88SZxS5KiWbuICIiIioxItIE4O8AThGRvSLS0B/7TZajosffU7YNy2pXNSpsFcUuRkkyZjFp7mgudlEAhKYTfeDSB7D0zKWhBJNUNHabHTZ76Tbf4mczIiok9sggIiIiskBVizIw+GD3wWLstmAEgqvqrsKpx56Kb/35W8UuTskxEp5OHTW12EWJuOS+S+C0O9GrA3PKynJhTK1JVCwuuwu1I2uLXQwA7JFBREREVNJe2/9asYuQVwrFL1/8JYMYSRg5MsZ7xmPx1MX9ss90CT17A73o9HX2S1mIqHQtnLywJPJjAAxkEBEREZU0tyP9bB80cPQF+uAL+LB662osql+Ex+Y+hobaBtw267aC7TNZklYiomi3Nd8Gb5e32MUAwKElRERERCVt+gnTcdfLdxW7GFQgDnHAr0dmmvnEiE/g7DvPTlivsbURxw89Hns+2tOfxSMiiggiiI07N+LSiZcWuyjskUFERERUyj7o/qDYRaACig5iAMA277ak6zKIQUTF9m7Xu8UuAgAGMoiIiIhK2kBL9kmxXHZXsYtARGRZ3ci6YhcBAAMZRERERCXtNe/ASvZJsXwBzkRBROWjVIKvDGQQERERlTARKXYRqB8JeL6JqHRxaAkRERERpTX141OLXQTqR0MrhuJPX/kTfnPBb1Bpryx2cYiIYjy4/cFiFwEAZy0hIiIiKmnvdpbG0y/qH32BPkwbNw3tB9vZG4eISs67H5XG3yQGMoiIiIhK2PN7ni92EaiABAKX3QWb2KBQNM5uxD/e/wfufeVedPu7i108IqIYFY6KYhcBAAMZRERERCWtyllV7CJQASkUvYHeyO8/WP8DTrNKRCXrX6r+pdhFAMAcGUREREQlrVQSq1H/YBCDiErZsVXHFrsIABjIICIiIippH/o+LHYRiIiIAABnHHdGsYsAgIEMIiIiopJ2lOuoYheBiIgIADDENaTYRQDAHBlEREREhfHOO4DVWSe+9S1g5crYZQsXAr/7HbZYePvNnwNumRa77NE/Al/8h7XdL7wI+F197LJNdwCT91l7/xfnAn8+JXZZxy+Aj3dae//khcCWj8cu05utvRcAPv5dYN+wI7+P/BB451fW3y9x+6p7B9i80nTVBO9UA6O+H7vsojeAx5qsvX/zSKD+qthl39oErPyztfc/djLwpX+NXXbTRuDm/7P2/pV1wFVfil12x6PAQisXHnjt8dqLXcZrbxBcezd/ERg5MvQ3LtpjjwFf+pL5e+LV1VlbLwX2yCAiIiIiKhE24e05EVE6/KYkIiIiIioRQQ0WuwhERCVPVLXYZci7+vp63bRpU163KbdY7BpaovSmgXeeiYgocyKyWVXr069JucrX/cisP8zC4289nocSERER5Wb7ou0Y7xmf83ZyvR9hjwwiIiKiEjbjhBnFLgIREREA4OG2h4tdBABM9jloZNKjhL03iIiISsfQiqHFLgIREVFIiQxUYI8MIiIiohLW7e8udhGIiIgAAF8+9cvFLgIABjKIiIiIStq4EeOKXQQiIiIsnro4L/kx8oFDS4iIiIhK2Cv/fKXYRSAiokHM7XBDofjM8Z8pdlEi2CODiIiIqIS9d/i9YheBiIgGsW5/N3r8PWhY2wBvl7fYxQHAQAYRERFRSdt5YGfS14Y4hvRjSYiIaDBz2p1oP9he7GIAYCCDiIiIqKS99cFbpstPPvpkPD3/adz+hdvhdrhR5ayClEo6eSIiKhl22POynb5AH2qG1+RlW7liIIOIiIiohE0eOdl0+ZmjzsSUUVNwVf1V2HXdLmyctxE3nH1DP5eOiIgA4LG5j+E3F/wGDimNNJR/+PIfcOM5N+K5K5+D/yY/bpt1Gz496tMZB7xddheGVQyD2+FG4+xGeKo8BSpxZkqjlomIiIjI1ML6hbj71btNlxs8VR54qjyodlXjZ8/9LGFdt8ONQDAAX9CXdn8OcUBVEUAgt4JT1oyGhkKLXBKi8ieQgn+WFk9djItOvggAcGzVsWhY2wCb2BDUIBrqGtC4pTHy+4kjTsSr3lcLXp6vffJrMcuumXIN6j9ejxn3zMCh3kOWt/W3b/wNLrsLNcNrSiaIATCQQURERFTSXHYXnOJEn/ZFljnFCZfdlbDueM94LJ66GLc23xpZtqB2Aa6efDV8AR/OvvPslPtyiAN/+de/4P92/Z9pQMRlc0Eg6A325nBExee0heqvvxoVmXr2ymfR8WEHLnvwsozed3Xd1Xix40WcOepM3L7l9gKVjqg02Yz/7Db4AkeCtpWOSvzui7/DxvaN2PzOZmx9d2vktdM9p+Mf7/8DAEy/1yrsFXDYHKYBiZ9M+wk8QzyYOmpqzJSkcyfOxfRx09F+sD3S+L/xnBtjfn9+9/N4uO1h/Lr51/AH/ZH3OmwOQI8EMVUVNgkd18IpC/Hb5t8iiCBssOGer9yDMcPGYP1b6zHzEzNxtPtoNHc0J5QnWs3wmpi6SWfx1MU4a8xZltfvTwxkEBEREVkkIhcA+DUAO4Dfq+p/FXqfNcNr4LA70Oc/Eshw2B1JxymvuHAFFtUvSrihbelogdvhRre/O7KuU5wQETjtTgSCAayaswozT5yJgz0HTbd907k34Vt138LXH/o61r+9PrJ85gkzsfyC5WhsbcQv//7L3A/aIqfNCQDoC/bFLBcIXHYXbGKLOV7DxnkbY54wPr/7eax/az1EBD9/7ucAQg2JYDAYE0BK51czfoXX9r+GxtbGhNf+/ex/x7td72LNtjXo6uuKLHfZXTENi+iGw7N7no0JSi2euhiL6hdhzbY1+OkzP43pNeMQBxbULcBvv/hbrN+xnoGMAeiCT1yAa6dei498H6FhbQOCGkRvILHx7RAHKhwVCAQDOGfsOTGf1YtOvAjHDT0Oc06dgyffejLm+jLjtDkBRUafg2gCQaWjEk67E32BPnx2zGdjygOEAqRQ4K6L78JQ11A88vojmHPqnMhxGu+9/jPXQ6GY+YmZWPPamtiA7aQFuLr+atQMr8GGnRti3tc4uxFzJ86N9FAwPu8zPzETZ405C94uL9oPtuP2zbdjVeuqyDYXT12cEICI/z0Zo5dcst/PGnMWzhpzFiaPmhzTe6NxdmNMEARA2v1HBxqSBTCiy9E4uzGmfowAjfF7sgBNqRHVgddlrb6+Xjdt2pTXbcotTJ6VjN408K4hIqKBSkQ2q2p9sctRjkTEDuAfAGYA2AugBcBcVd1utn4+70eatjWZ3phnwtvlxdjlY2Ma9m6HG5sXbkanrzPmxtjb5cXHfvExBBGMrGuDDf/8/j8j68Q3BgBg/Y71OP/e89OWZahrKPoCfQgiGNOId4oTDrsj4Ya6L9CHb/35Wwnb+dNX/oThlcMxe81s9AR6TI/rjs13xAQWFk9djBUXrkhZT0ZDwWgQ2W12dPo6Ux6Tw+bAO999B/sP78eE2yYkvL590XYcO+RY03Pw1BVPYccHO0wbDm3etoSgVLJzueu6XfBUeSyfh1wJBC6bCw67Az19PQUdjuSUcNAqRYM6fgiBy+7Cg5c+iJaOFsz8xEwAwPq31uPkY07G1x/+etLtGI3KZNuN5rK7oAGF2CTSGJ903CTTYFMuXHYX9i7dG/MZ3bhzo2mvneeufC4mUGd2DRnavG3Y8PYGfPuJbydsZ/ui7ZHvhg07N2D+Q/MR0EDCMTnFiY3zN2LHBzvgPezFj//248hrq+asSuidEF2eY4ccmzIwEP15jH891XGlel8qqbZZKNmWNZ/7LEYZcr0fYSDDIgYykmMgg4iofDCQkT0R+TSAm1X1/PDvNwCAqv6n2fr5vh/Jx41mJgGRpm1NuPLhKyEiUFXc+eU70wZPvF1ejPrlqJjGpg02OG3OyHZ+PevXqPtYXdInp/GNHmO7o5eNjgl6RDfs0h1XLo0To9637NuCpU8ujXmK+fstv480clfNXhXZ55LHlyT0pDCCJ/kISqXbjll9OcSBX53/K0w/YTpu23Rb2ifx0SpsFXDYHej19wIC2CU0A8Kdc+6MnK9qVzVOu+20jHIROGwOiIbuseOHTkUHtJadvwx1I+tQ7arG5JWTYwI4FbYKLP7UYnz51C9j94e7E55uJ6vb+HMULT64tPXdrZHt+gI+QAG3y532mk0VtPIe9uLGjTfCJjYENACf35cQOHQ5XLCLPeWxpLrWMtG0rQnzHpwHFYWoYPVXVifsL/qzcN0T18Fus0d6ckWvW4xGMZUfBjJMMJDRvxjIICIqHwxkZE9ELgFwgap+M/z7FQA+paqLzdYvxP1IPmTSyMimQdK0rQkLHlkQ08gxa+hlug+j4Z6skdofjadMnmIW4mlxuvJES1df0eXb+u5WzH94PhSKQDAAgcBms0EguOvLd6Xs6h6/z/mPzEdQg7CJDTecfQNUQ0MBdn+4O+a6WH7h8owDWtHHlSwQlEndGnXg7fLixqdvTBlcit5uqjowOwdWyrph54aMPjdmx5FrL4JCfzcQRWMgwwQDGf2LgQwiovLBQEb2rAQyRGQhgIUAMGbMmMm7du0qSlmLrVCNHDaeMpNtwxSw1lDPdJ/ZvpbLceWj3P21XV7fNJgwkGGCgYz+xUAGEVH5YCAje8UeWkJERDRQ5Ho/YstnYYiIiIgGsBYAJ4nIOBFxAbgcwKNFLhMREdGgw+lXiYiIiCxQVb+ILAbwJELTr65S1deKXCwiIqJBh4EMylmmw244FIWIiMqVqq4DsK7Y5SAiIhrMOLSEiIiIiIiIiMoGe2RQv2MPDiIiIiIiIsoWe2QQERERERERUdkom0CGiFwgIm+IyA4R+WGxy0NERERERERE/a8shpaIiB3A/wKYAWAvgBYReVRVtxe3ZNQfOBSFiIiIiIiIDGURyAAwFcAOVX0bAERkDYDZABjIoASZBj4yxUAJERERERFR8ZRLIGMUgD1Rv+8F8KkilYUGuUIHSgqJQRgiIiIiIip35RLISEtEFgJYGP61U0TeyHGTxwLYn+M2StFAPS5g4B5b3o5Lbi65IAzPWfkZqMc2mI5rbDEKMhht3rx5v4jsyvNmB+q1mi+sn9RYP6mxftJjHaXG+kktun5yuh8pl0BGB4Djo34fHV4WoaorAazM1w5FZJOq1udre6VioB4XMHCPbaAeFzBwj22gHhcwcI+Nx0WFoKqefG+T5zQ11k9qrJ/UWD/psY5SY/2kls/6KZdZS1oAnCQi40TEBeByAI8WuUxERERERERE1M/KokeGqvpFZDGAJwHYAaxS1deKXCwiIiIiIiIi6mdlEcgAAFVdB2BdP+4yb8NUSsxAPS5g4B7bQD0uYOAe20A9LmDgHhuPi8oFz2lqrJ/UWD+psX7SYx2lxvpJLX+pIFQ5iwERERERERERlYdyyZFBRERERERERMRARjwRuUBE3hCRHSLyw2KXJ19EZJWIvCci24pdlnwSkeNFZKOIbBeR10TkO8UuU76ISKWINIvIy+Fju6XYZconEbGLSKuI/LnYZcknEWkXkVdFZKuIbCp2efJFRIaLyAMi8rqItInIp4tdpnwQkVPC58r496GIXFfscuWDiCwNf3dsE5EmEaksdpkokdnfZxE5Q0T+Hv4ueUxEhkW9dkP4HuUNETk/avmAvH8BMqsjEZkhIpvDyzeLyOej3jM5vHyHiPxGREpuTvJsZHoNhV8fIyKdIvL9qGUD8hrK4jP2yfBrr4VfrwwvH/TXj4g4RWR1eHmbiNwQ9Z6Bev2YtjVE5GgReUpE3gz/f0R4uYSvjx0i8oqI1EVta154/TdFZF6xjimfsqifr4Xr5VUReUFEzojaVmbXkKryX/gfQolE3wJwAgAXgJcBTCh2ufJ0bOcAqAOwrdhlyfNxjQRQF/55KIB/DKBzJgCqwz87AbwE4MxilyuPx/ddAH8E8OdilyXPx9UO4Nhil6MAx7UawDfDP7sADC92mQpwjHYA/wQwtthlycOxjAKwE4A7/Pt9AOYXu1z8Z3quEv4+IzRb2+fCPy8A8P/CP08I35tUABgXvmexD+T7lyzqqBbAx8M/TwTQEfWeZgBnhv++Pg7gwmIfW3/XT9TrDwC4H8D3w78P2Gsow+vHAeAVAGeEfz8GgJ3XT6R+/hXAmvDPQ8L3PDUD/PoxbWsA+G8APwwv/yGAn4d/nhW+PiR8vbwUXn40gLfD/x8R/nlEsY+vCPXzGeO4AVwYVT8ZX0PskRFrKoAdqvq2qvoArAEwu8hlygtVfQbAB8UuR76p6j5V3RL++SMAbQjdwJc9DekM/+oM/xsQSW1EZDSALwD4fbHLQumJyFEI3eg0AoCq+lT1YFELVRjnAXhLVXcVuyB54gDgFhEHQjec7xS5PGQiyd/nkwE8E/75KQBfCf88G6FGRK+q7gSwA6F7lwF7/wJkVkeq2qqqxrX+GkKfgQoRGQlgmKq+qKG75rsBzCl44ftBhtcQRGQOQoHO6BkAB+w1lGH9zATwiqq+HH7v+6oa4PUTqR8FUBX+u+IG4APwIQb29ZOsrTEboYc8CP9/Tvjn2QDuDt/HvwhgePj6OR/AU6r6gaoeQKheL+i/IymMTOtHVV8IHz8AvAhgdPjnjK8hBjJijQKwJ+r3vRggjeLBQERqEHoS81KRi5I3Ehp+sRXAewh9+Q2UY1sO4HoAwSKXoxAUwPpwl+aFxS5MnowD4AVwp4SGA/1eRKqKXagCuBxAU7ELkQ+q2gHgFwB2A9gH4JCqri9uqSgDr+HIDdxXARwf/jnZfcpgvH9JVkfRvgJgi6r2IlQfe6NeG+h1ZFo/IlIN4AcA4oerDrZrKNn1czIAFZEnRWSLiFwfXs7rJ+QBAF0I/V3ZDeAXqvoBBsn1E9fWOE5V94Vf+ieA48I/D9rvaYv1E60Bod4rQBb1w0AGDQjhP8wPArhOVT8sdnnyRVUDqjoJoWjlVBGZWOQi5UxELgLwnqpuLnZZCuRsVa1DqLvctSJyTrELlAcOhLqd/lZVaxG6iRkw418BQERcAL6EUFfrshceizoboSDUxxF6gvb14paKMrAAwCIR2YxQV11fkctTilLWkYicBuDnAK4qQtlKQbL6uRnAsqgen4NVsvpxADgbwNfC//+yiJxXnCIWVbL6mQoggNDflXEAviciJxSniP0rVVsj3EtnQPSazlam9SMi0xAKZPwg2306sn3jANWB2Ij+6PAyKmEi4kTog3Ovqj5U7PIUgqoeFJGNCHVBK/eErWcB+JKIzAJQCWCYiPxBVQdEIyv8JByq+p6IPIzQH/1nUr+r5O0FsDeqR9ADGGCBDIQCT1tU9d1iFyRPpgPYqapeABCRhxAal/qHopaKLFHV1xHq4g4RORmhoXhA6vuUQXX/kqKOjOGLDwP4hqq+FV7cgSNdmIEBXkcp6udTAC4Rkf8GMBxAUER6AGzGILqGUtTPXgDPqOr+8GvrEArk/wG8foBQjownVLUPwHsi8jyAeoSepA/Y6ydJW+NdERmpqvvCQ0feCy9P9j3dAeDcuOVPF7Lc/SXD+oGIfBKh4eUXqur74cUZt8PZIyNWC4CTRGRc+Onc5QAeLXKZKAUREYTG7bep6q+KXZ58EhGPiAwP/+wGMAPA60UtVB6o6g2qOlpVaxD6jP1toAQxRKRKRIYaPyN0E1DugSeo6j8B7BGRU8KLzgOwvYhFKoS5GCDDSsJ2AzhTRIaEvyfPQ2jcKpUBEfmX8P9tAP4/ALeHX3oUwOXhnA/jAJyEUALCQXf/kqyOwn83/4JQkrnnjfXDXZw/FJEzw5+JbwBY29/l7i/J6kdVP6uqNeG/wcsB/ExVb8Ugu4ZSfMaeBHB6+LvTAeBzALbz+onUz24Anw+/VoVQMsvXMYCvnxRtjUcBGDOPzMOR6+FRAN+QkDMRGtq5D6Fra6aIjAj3mpwZXlbWMq0fERkD4CEAV6jqP6LWz/gaYo+MKKrqF5HFCF1UdgCrVPW1NG8rCyLShFAU8FgR2QvgJlVtLG6p8uIsAFcAeDWcSwIA/l1V1xWvSHkzEsBqEbEjFHS8T1UH1FSlA9BxAB4OfafDAeCPqvpEcYuUN0sA3Bv+4/I2gCuLXJ68Cd+MzcAA6oKuqi+JyAMAtgDwA2gFsLK4pSIzZn+fAVSLyLXhVR4CcCcAqOprInIfQoFEP4BrVTUQ3s6AvH8BMqsjAIsBnAjgRhG5Mbxspqq+B2ARgLsQSlL4OI6MzS5rGdaPqcF2D4zkn7EDIvIrhBpVCmCdqv4lvB6vH+B/EcqX9RpCs3LcqaqvhLczIK8fJGlrAPgvAPeJSAOAXQAuDb+2DqGZS3YAOIzw/ZKqfiAi/w+hawsAfhLOL1LuMq2fGxGaDei28P2yX1Xrs/kOktCQFSIiIiIiIiKi0sehJURERERERERUNhjIICIiIiIiIqKywUAGEREREREREZUNBjKIiIiIiIiIqGwwkEFERFQGRGSViLwnIpam9BWRS0Vku4i8JiJ/LHT5iIiIiPoLZy0hooIRkZ8AeEZVNxS7LETlTkTOAdAJ4G5VnZhm3ZMA3Afg8+HpBP8lPP0kERERUdljjwwiKggRsavqjQxiEOWHqj4DIGbOeRH5hIg8ISKbReRZETk1/NK3APyvqh4Iv5dBDCIaVESkxmoPtqj3fElEfhj++WYR+X745/ki8vFClJOIssNABhFlLHxz8LqI3CsibSLygIgMEZF2Efm5iGwB8FURuUtELgm/Z4qIvCAiL4tIs4gMFRG7iPyPiLSIyCsiclWRD42o3KwEsERVJwP4PoDbwstPBnCyiDwvIi+KyAVFKyERUQGJiCNf21LVR1X1v0xemg+AgQyiEpK3Dz4RDTqnAGhQ1edFZBWAReHl76tqHQAYjScRcQH4E4DLVLVFRIYB6AbQAOCQqk4RkQoAz4vIelXd2e9HQ1RmRKQawGcA3C8ixuKK8P8dAE4CcC6A0QCeEZHTVfVgPxeTiCgn4WGqH6jq8vDvPwXwHoBLABwAcCpCwVszDhG5F0AdgNcAfENVD4tIO4B6Vd0vIvUAfqGq54rI/PDyxVH7vwRAPYB7RaQbwKdVtbsAh0pEGWCPDCLK1h5VfT788x8AnB3++U8m654CYJ+qtgCAqn6oqn4AMwF8Q0S2AngJwDEINb6IKD0bgIOqOinq3/jwa3sBPKqqfeHA4D/AzxYRladVAL4BACJiA3A5Qt9xdQC+o6rJghhA6P7jtvB344c48tDFMlV9AMAmAF8Lf88yiEFUAhjIIKJsxWcKNn7vymAbglC3eKMRNk5V1+eneEQDm6p+CGCniHwVACTkjPDLjyDUGwMicixCTyvfLkIxiYhyoqrtAN4XkVqEHoC0AngfQLOFHpzJHroQUZljIIOIsjVGRD4d/vlfATyXYt03AIwUkSkAEM6P4QDwJIBrRMQZXn6yiFQVstBE5UpEmgD8HcApIrJXRBoAfA1Ag4i8jFC36dnh1Z9E6MZ/O4CNAP5NVd8vRrmJiPLg9wjlqbgSoR4agLUHJ8keuvhxpB1UmWvhiKj/MUcGEWXrDQDXhvNjbAfwWwBLzFZUVZ+IXAZghYi4EcqPMR2hG5MaAFskNMjfC2BO4YtOVH5UdW6SlxISeWpobvXvhv8REZW7hwH8BIAToYcnn7X4vjEi8mlV/TtiH7q0A5gM4HEAX7GwnY8ADM2kwERUWAxkEFG2/Kr69bhlNdG/qOr8qJ9bAJxpsp1/D/8jIiIiShB+ILIRobxAgagEx+mYPXQBgFsANIrI/wPwtIXt3AXgdib7JCodEnpoQ0RknYjUAPizqk4sdlmIiIhoYAsn+dwC4Kuq+maxy0NExcccGUSUMVVtZxCDiIiICk1EJgDYAeCvDGIQkYE9MoiIiIiIqCyJyDEA/mry0nlMckw0cDGQQURERERERERlg0NLiIiIiIiIiKhsMJBBRERERERERGWDgQwiIiIiIiIiKhsMZBARERERERFR2WAgg4iIiIiIiIjKxv8PdEcWhOjxlVsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT DISTRIBUTIONS ###\n",
    "\n",
    "plt.figure(figsize=(18,6))\n",
    "\n",
    "plt.subplot((121))\n",
    "df['price'].plot.hist(bins=35, ax=plt.gca(), color='green')\n",
    "plt.xlabel('price')\n",
    "\n",
    "plt.subplot((122))\n",
    "df.plot.scatter(x='yr_built', y='price', ax=plt.gca(), color='green')\n",
    "plt.axhline(df['price'].median(), c='red', \n",
    "            linestyle='--', linewidth=3, label='median price')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae133e96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:26.207807Z",
     "iopub.status.busy": "2023-01-08T20:59:26.207398Z",
     "iopub.status.idle": "2023-01-08T20:59:26.423355Z",
     "shell.execute_reply": "2023-01-08T20:59:26.422115Z"
    },
    "papermill": {
     "duration": 0.225093,
     "end_time": "2023-01-08T20:59:26.426023",
     "exception": false,
     "start_time": "2023-01-08T20:59:26.200930",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### HIGHEST AND LOWEST MEDIAN SELLING PRICES BY YEAR ###\n",
    "\n",
    "df.groupby('yr_built')['price'].median().sort_values() \\\n",
    "    .iloc[[0,1,2,3,4,-5,-4,-3,-2,-1]].plot.bar(figsize=(16,6), color='green')\n",
    "plt.axhline(df['price'].median(), c='red', \n",
    "            linestyle='--', label='median price')\n",
    "plt.legend(); plt.ylabel('price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e88b1bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:26.438867Z",
     "iopub.status.busy": "2023-01-08T20:59:26.438452Z",
     "iopub.status.idle": "2023-01-08T20:59:28.049966Z",
     "shell.execute_reply": "2023-01-08T20:59:28.048621Z"
    },
    "papermill": {
     "duration": 1.621989,
     "end_time": "2023-01-08T20:59:28.053339",
     "exception": false,
     "start_time": "2023-01-08T20:59:26.431350",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:01<00:00, 628.14it/s]\n"
     ]
    }
   ],
   "source": [
    "### UNIVARIATE SIMULATION FOR A SINGLE YEAR ###\n",
    "\n",
    "year = 2015\n",
    "\n",
    "sampling = lambda x,y: x['price'].sample(n=int(y['count']))\n",
    "\n",
    "y = df[df['yr_built'] == year]['price'].agg(['count','median'])\n",
    "\n",
    "observed_diff = abs(y['median'] - df['price'].median())\n",
    "\n",
    "sim_diffs = np.asarray([\n",
    "    abs(sampling(df,y).median() - df['price'].median())    \n",
    "    for i in tqdm(range(1_000))\n",
    "])\n",
    "\n",
    "p_value = np.mean(sim_diffs >= observed_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7a0db8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:28.068614Z",
     "iopub.status.busy": "2023-01-08T20:59:28.068195Z",
     "iopub.status.idle": "2023-01-08T20:59:28.303304Z",
     "shell.execute_reply": "2023-01-08T20:59:28.302141Z"
    },
    "papermill": {
     "duration": 0.245974,
     "end_time": "2023-01-08T20:59:28.305943",
     "exception": false,
     "start_time": "2023-01-08T20:59:28.059969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT UNIVARIATE SIMULATION FOR A SINGLE YEAR ###\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "plt.hist(sim_diffs, bins=25, color='green')\n",
    "plt.axvline(observed_diff, linestyle='--', linewidth=3, c='red', \n",
    "            label='observed difference')\n",
    "plt.title(f'year building: {year}')\n",
    "plt.legend(); plt.xlabel('simulated differences'); plt.ylabel('frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e6d1475",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T20:59:28.321256Z",
     "iopub.status.busy": "2023-01-08T20:59:28.320827Z",
     "iopub.status.idle": "2023-01-08T21:00:58.626535Z",
     "shell.execute_reply": "2023-01-08T21:00:58.625290Z"
    },
    "papermill": {
     "duration": 90.317833,
     "end_time": "2023-01-08T21:00:58.630608",
     "exception": false,
     "start_time": "2023-01-08T20:59:28.312775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 116/116 [01:30<00:00,  1.28it/s]\n"
     ]
    }
   ],
   "source": [
    "### UNIVARIATE SIMULATION FOR ALL THE YEARS ###\n",
    "\n",
    "test_diff = {}\n",
    "\n",
    "for y_b in tqdm(df['yr_built'].unique()):\n",
    "\n",
    "    y = df[df['yr_built'] == y_b]['price'].agg(['count','median'])\n",
    "\n",
    "    observed_diff = abs(y['median'] - df['price'].median())\n",
    "\n",
    "    test_diff[y_b] = np.mean([\n",
    "        abs(sampling(df,y).median() - df['price'].median()) >= observed_diff\n",
    "        for i in range(500)\n",
    "    ])\n",
    "    \n",
    "test_diff = pd.Series(test_diff).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3e9cc1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T21:00:58.660910Z",
     "iopub.status.busy": "2023-01-08T21:00:58.660497Z",
     "iopub.status.idle": "2023-01-08T21:00:58.834428Z",
     "shell.execute_reply": "2023-01-08T21:00:58.833410Z"
    },
    "papermill": {
     "duration": 0.191941,
     "end_time": "2023-01-08T21:00:58.837196",
     "exception": false,
     "start_time": "2023-01-08T21:00:58.645255",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT UNIVARIATE SIMULATION FOR ALL THE YEAR ###\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "plt.scatter(df.groupby('yr_built')['price'].median(), test_diff, c='green')\n",
    "plt.axvline(df['price'].median(), c='red', \n",
    "            linestyle='--', linewidth=3, label='median price')\n",
    "plt.legend(); plt.xlabel('price'); plt.ylabel('p-value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "353a507a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T21:00:58.870656Z",
     "iopub.status.busy": "2023-01-08T21:00:58.869602Z",
     "iopub.status.idle": "2023-01-08T21:01:00.115010Z",
     "shell.execute_reply": "2023-01-08T21:01:00.113265Z"
    },
    "papermill": {
     "duration": 1.265701,
     "end_time": "2023-01-08T21:01:00.118407",
     "exception": false,
     "start_time": "2023-01-08T21:00:58.852706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jbpXkC5O8spSSJHdKckEp5ZG11ksmLBcAAAAcKOX8csKyerSuoSTAnEwZ+Ls4yd1LKXfLEPB7VJJvO/bPWuuHkpx27HUp5ZVJflDQDwAAgLkQ0APWabKf+tZab0jyxCQXJbkyyYtqrZeXUp5eSnnkVOsFAAAAAKY94y+11guTXLi07GlbpH3QlGUBAAAAgMNkbTf3AAAAAACmM+kZfwAAADA3rtsHbApn/AEAAADADAn8AQAAAMAM+akvAAAAh96qn+8mfsILbDZn/AEAAADADAn8AQAAAMAMCfwBAAAAwAwJ/AEAAADADAn8AQAAAMAMCfwBAAAAwAwJ/AEAAADADAn8AQAAAMAMCfwBAAAAwAwJ/AEAAADADB1ZdwEAAABgKuX8csKyerSuoSQAJ58z/gAAAABghgT+AAAAAGCGBP4AAAAAYIYE/gAAAABghgT+AAAAAGCG3NUXAACAjbLqTr2Ju/UCLHPGHwAAAADMkMAfAAAAAMyQwB8AAAAAzJDAHwAAAADMkMAfAAAAAMyQwB8AAAAAzJDAHwAAAADMkMAfAAAAAMyQwB8AAAAAzJDAHwAAAADMkMAfAAAAAMyQwB8AAAAAzJDAHwAAAADMkMAfAAAAAMyQwB8AAAAAzJDAHwAAAADM0JF1FwAAAACSpJxfTlhWj9Y1lARgHpzxBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzdGTdBQAAAGC+yvnlhGX1aF1DSQAOn0nP+CulnFtKuaqUcnUp5akr/v/9pZQrSimXlVJeXkq565TlAQAAAIDDYrLAXynl1CTPTvLwJGcneXQp5eylZJcmOafWeq8kL07y36YqDwAAAAAcJlP+1Pd+Sa6utV6TJKWUFyY5L8kVxxLUWl+xkP41Sb59wvIAAACwT/yEF+Dgm/KnvndO8s6F19eNy7byXUn+aNU/SilPKKVcUkq55Prrr9/HIgIAAADAPB2Im3uUUr49yTlJHrjq/7XW5yZ5bpKcc845vkICAACYgLP4AOZlysDfu5LcZeH1GeOy45RSHpLkR5I8sNb6TxOWBwAAAAAOjSl/6ntxkruXUu5WSrlxkkcluWAxQSnlS5L8YpJH1lr/bsKyAAAAAMChMlngr9Z6Q5InJrkoyZVJXlRrvbyU8vRSyiPHZP89yS2T/E4p5Y2llAu2yA4AAAAA6DDpNf5qrRcmuXBp2dMWnj9kyvUDAAAAwGF1IG7uAQAAwDRW3bAjcdMOgMNgymv8AQAAAABrIvAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAMyTwBwAAAAAzJPAHAAAAADMk8AcAAAAAM3Rk3QUAAABgUM4vJyyrR+ue0wJwODnjDwAAAABmSOAPAAAAAGbIT30BAAAm5me5AKyDM/4AAAAAYIYE/gAAAABghgT+AAAAAGCGBP4AAAAAYIYE/gAAAABghtzVFwAAYBfcqReAg07gDwAAmL3WIN2qdFulBYCDzk99AQAAAGCGBP4AAAAAYIYE/gAAAABghgT+AAAAAGCG3NwDAADYSG7EAQDbc8YfAAAAAMyQM/4AAIADZdWZfM7iA4B+zvgDAAAAgBkS+AMAAACAGfJTXwAAYHJ+vgsAJ58z/gAAAABghgT+AAAAAGCGBP4AAAAAYIZc4w8AANg11+4DgIPLGX8AAAAAMEMCfwAAAAAwQwJ/AAAAADBDrvEHAAAcx3X7AGAenPEHAAAAADPkjD8AADgEVp3FlziTDwDmTOAPAAA2mJ/lAgBbEfgDAICTpDVIJ5gHAOwHgT8AAA6NvQTeetIK0gEAB4GbewAAAADADAn8AQAAAMAM+akvAAAnTc/PYqf4WS4AwGEi8AcAcIhMde0617kDADh4BP4AAGZA4A0AgGUCfwAAB5RgHgAAezFp4K+Ucm6Sn01yapJfrrX+1NL/b5Lk+Unum+R9Sb611nrtlGUCAOZpip+w+lksAACbbLLAXynl1CTPTvLQJNclubiUckGt9YqFZN+V5AO11n9eSnlUkmck+dapygQAbB5BMgAA2J0pz/i7X5Kra63XJEkp5YVJzkuyGPg7L8mPjc9fnORZpZRSa3U0D3DAnKw7cZ7Mu3tuyllfm9JOe12/YB4AAOyvMlWMrZTyTUnOrbX+m/H1dyT5slrrExfSvGVMc934+q/GNO9dyusJSZ4wvvy8JFctre60JO9NmynSHuY8171+ddJO+5nnute/KXmue/3qpJ32M891r39T8lz3+jclz3WvX520037mue71b0qe616/Ommn/cxz3evflDzXvf6t0t211nr6CUtrrZM8knxThuv6HXv9HUmetZTmLUnOWHj9V0lO28W6Llln2sOc57rXr07aaU7r35Q8171+ddJOc1r/puS57vVvSp7rXr86aac5rX9T8lz3+tVJO81p/ZuS57rX35NnrTWnZDrvSnKXhddnjMtWpimlHElymww3+QAAAAAA9mDKwN/FSe5eSrlbKeXGSR6V5IKlNBckeez4/JuS/Gkdw5cAAAAAwO5NdnOPWusNpZQnJrkoyalJnldrvbyU8vQMpyVekORXkvx6KeXqJO/PEBzcjeeuOe1hznPd61en9eW57vWr0/ryXPf61Wl9ea57/eq0vjzXvf5NyXPd61en9eW57vWr0/ryXPf61Wl9ea57/eq0vjzXvf6ePKe7uQcAAAAAsD5T/tQXAAAAAFgTgT8AAAAAmCGBPwAAAACYIYE/AAAAAJihye7qCxxepZR/meQ9tdarSilfkeTLk1xZa/3fe8jzNknOTXLncdG7klxUa/3gXss7pVLKT9Zaf3jd5QAAANhUpZQ7JUmt9W9LKacn+X+SXFVrvXy9JTv43NWXlUopz6+1Pmbd5eDgKKXcL0mttV5cSjk7QxDurbXWC5fS/UyS+2X4YuGiJA9O8kdJHpjk0lrrf9zFuh+T5GiSl2UI+CXJGUkemuT8Wuvzd1Wprdf30FrrH+/ifc9cXpTkO5I8P0lqrU9aSHtmkr+rtX6slFKSPC7JfZJckeSXaq037LL4B0Yp5QEZxsJbaq0vW3d5WK2UcrckX5LkilrrW9ddHpijUsojk7ys1vqxdZdlHUopt8xw3HCXJJ9M8rYM7fGptRYMYB+UUh5fa/2f6y7HJiql3DrJ6bXWv1pafq9a62ULr/9tkqdm+Hz1jAyfnd6S5AFJ/lut9VcW0t4zyXk5/oSRC2qtV+6hnPcc83ttrfXvF5afW2t96W7zPWlqrRv1SHL7JE9L8m8ydPqPJHlJkv+e5HYN73/+FstvnOQxSR4yvv62JM9K8r1JbrRDnn+6l7Im+ekkX9FY/29Icvvx+ekZAgpvTvLbSc5YSnva0utvT/LMJE/IGPQdl1+w9PjDJH9/7PWKMnzl2DZ/kOR3k/xUkn++It1XJ/mFhXx/Icm5W9Trq5N8V5KzlpZ/58LzkuRbknzz+PzBY32+J8kpe2innva/X5IvHZ+fneT7kzxii7T3HMt4y6Xl5y48f1KSu+zDdvG03da/Jc8MQbfXJLkkyX9N8qdJ/nOSP0/yI0vvu3zsn5sn+UCSm4/Lb5QhALS8ns9N8oNJfnbsi3+X5NZLaa5KctsV771dkrftdoxuU/d37PD/B4x9/7Cl5e9M8hsZ5pLHjo/rjz1fSvuWhbZ5RpIXZ9hGn5fkeQ1lXDmXdY6TeyZ5yrgdPXN8/vkr3te0jSR53cLz707yxnHsvCrJU1ek33G7X0jXOpfsuN011OPxC8+/7Nh4THKzJOdnmCOfkeQ2S+9r2o+kc7tvHc9pnCOT/P7C8/OS/HWS/zluZ4/bop+a2r9l3O0lz2yxv+0Zz61tmsZ96ML/W+ayrjxb65/2441dH+ts1ae7aKeWfWPXsV5L2/eMk3Rso2mfH/8xyXuT/HqSRyQ5dZu0zfVP+3bfdVyQxvl5mzoszqPfkuR1SX45yV+NbfCCJJcl+aKl9zXPuT3lbB2nve20lOfKY4PWdpqq71vHfe/Y76l7+j47tPRn73huXX/zcf6K955wXLaL8dw0l/X0fWs/7TbP8b1b7psnaqfm+am1T7P3Y513LL1u3jem47PomH5Px7pZfVy2q7Hf2fcnpB3H3N9k+Mxw+bEyjP97w1LaN2f4fHmHDLGKO43Lb5fkjQvpnjLm99Sx3b99fP7GrPg8sk15F/djT8pwnPz7Sa5Nct5W5ewZT+k4LusdJ8uPjTvjr5RyYYZOv3WSzx+fvyjDmT9fXGs9byHtBctvzzDx/2mS1FofuZD2BRnOULp5kg8muWWGHcODM2ycjx3TXXZ8lilJ7pFhIKTWeq/espZSrk/y9gw7rt9O8lu11ku3qP8Vtdazx+e/nSEY8ztJHpLkX9daH7qQ9g211vuMz380w6mwv5nka5NcV2v9vmPpMpxl9MtJ6lin30ryqLFOf7aQ539NcqckL0/y9Rk+ML4tw07hJ2utvzOm+5mxXZ6f5Lrx7WdkGNh/WWt98kKeP5lhR/SGJF+X5GdqrT+3og4/n+SzMmwgH05ykwwb0tdk+FnpYp497dTU/qWUo0kenmGc/HGGndQrMvTnRbXWn1hI+6QMG+yVSe6d5Mm11j9YUacPJflohgPh30ryO7XW65fXvZNSyjtqrWfupv4teZZS3jzW4yZJ/jbDQdWHSyk3y/Ctx+K4f0ut9QtLKTdN8u4kn1Nr/cdSyqlJ3nysXAvt9LUZAoiPSHJphu3vG5J8T631lWO6t2XYEXxoqYy3SXJJrfXuC8tax+jy/PDpLJJ8Va31Fgt5vq7Wer/x+Xdn6NvfS/KwJH9Ya/2p8X+3SvLjGcbpD9Za/6aUck2t9XNXtO9iH71+rN+nxtdvqrV+8ULa5rlsO0t9+pQkj07ywhy/jT4qyQuP1WlM27qNXFpr/ZLx+cUZDhiuL6XcIslraq1ftJC2dbv/mbTPJU3bXWc7XZ5hvr6hlPLcJP+QIUj74HH5v1p4X+t+pHm7bx3PY9qmOXKpn16dYU7461LKaUlevjT2fiaN7d/Snj15du5ve8Zz6xzRtA8d/986l/XkOcXxRtMY3cnSNtLbTi37xp5jvaa2H9M2jZPObbR5fkzyVUm+aVzfF2bYj/zW4nHWLurfut33HBc1zc/bWRojlyW5f631H8Z55gW11q8updwryXNqrf9i4X09c25zOTuOiXvaqenYoLWdxtdT9H3P/Ng09nvq3jHn9vRnTz+1rr/nOL/puKxzPPfMZa1939NPrXn27JumaKeecdLUp3s4LjmuDWqtN1lYd8++sScWsOdj3RXzTms79fR9U9pSyhuTPLzW+u4y/Lrs+Ul+qNb6e4vHq8v1W/FZafHY9m1JvqDW+omlet84yeV14XNjazuV4bPwl9da/76UclaG8fnrtdafXVHOn0n7Z5fm47KecbJS3WXEcF2PjNHcDIPnXav+t/D6DRnOvnlQhp8ZPihDIOKBSR64lPay8e+RJO/J+E3suJ7LFtJdMOZ5zyR3TXJWhrN87prkrrspa4afP2YcIP85Q7T7rRnOlLnH0vuuWnj++h3qf+lSW9xifH6jDAGYY/87Jcn3ZdjQ7z0uu2aL9l9835Ekrxqf3y4LZ3NlxVlYC23xl8t5JjkyPr9tkguT/I8VdXjzQvnfl+TGC+W4bCnP7nbaqf3Hcp6aYcP8cI7/Zmp5/W/O+C3MOEYuyTAxL9fp0rH9H5bkVzKcHfbSDGeI3Wopzw9v8fhIkht2U//WPJfLvEN7PiPJ/0lycYZvqP8wwzfWL8twgL/cTse2tZsneeX4/MyldT42w0HoLyT54fHxnHHZ43Y5Rj+Q4WDmgUuPB2U4wNlqW7o4w+noSXKLxfUtpLlvhh3mDya5dott4aIMAcYk+V8Z548M32K9aSltz1zW2qdvy4ozfDIc8C1vo63byJvGdr5DhoDsyjbs3O5755Idt7vx9WVbPN6c5J8W0l252A87jP3W/UjPdt80nhfTZoc5crEeWThLc4t2amr/1nHXmWfP/rZnPLfOEYvjcMt96MLYa5nLevKc4nijaYz29Oku2qll39hUn5627xkn6dtGLx3/7jQ/Ls8fd8pwBsH/TfLO3fTn4njOztt9z3FR6/zcOo++Ofn0yQY3W8pjeR7rmXObytkzTjvbaTHPLY8NWttpwr7v2t+nYey31n2xnAtl22rO7enPrvHcsf7W4/ym47L0j+fWuay177v7qSHPnn3TVO3UOk6a+jTtxyXvyRBsu+vS46wkf7O8LS21w3b7xkvHvy2xgNb9aM9xWWs79fR9U9oVbfHZSV6fYf+4PBZen3Euy8KZvUlumoXPTmPbHVeecfldszB3jMta92OXL73vlhnmxp/OiWO057NLz3FZ8zhZ9Tglm+eUUsrtMlwj5JZjxDWllDtk2IEtOifDAPmRJB+qwzcl/1hr/bO69O3qmO+Nk9wqw6C/zbj8Jhk20CSf/mbifyV5boZvIK5N8ola69trrW/fZVnrmPfbaq0/Xmv9ggynvd40w2S26JWllKeX4UyrV5ZSvmHM8yuTfGgp7c1KKV9SSrlvhkH00XE9n8hwfZVjdfpUrfV/JHl8kh8ppTwrW9/45VOllNuPzz8nwySRWusHMgzQYz5WSvnSFe//0iTL17c5UsfrmdXhRg1fl+TWpZTfyfHtdCzNJ5JcXGv9+Pj6hiTL14jpaafW9r+h1vrJWus/JPmrWuuHx/f944r1n1LH3/6PY+RBSR5eSvnpHN9OdWz/l9VavytDm/58huvgXLOU5weT3L3Weuulx60y7ER3U//WPD9eSrn5+Py+xxaW4Yy74+pea31Kkv+U5N/X4Xp+/yHJP2U4o/R7cqJjY+0mGSbR1FrfkeO3u1/LsD3/2ZjXPyV5ZZJzaq2/upRf6xh9TZJ/ODYfLDxemfHbqAWnlFJuN267pY7ffo/b1AnX4qu1vj7DmR3/mOQvVtQ5GX7G859LKX+eYZy/sZTyiiR/kuH0+kU9c9kH09annxrbZ9ln58Tx3LqN3GYs5yVJbl9K+ewkKcO1ncrxWTZv9z1zSet2lyR3zPDN29eteLxvId1bSimPH5+/qZRyzlineyQ57pvENO5H0rfdt47npH2O/OJSyodLKR9Jcu+FfrrxsfwXtLb/B9M+PzXl2bm/7RnPrW3atA9dsONc1pPnRMcbrWM0ae/TnnZq3UZ7jvWStrZP2sdJzzbaOj8et73WWv+21vrMWuuXZzh7ZVFP/Vu3+57jotb5uXUevTDJS0spx74EPHaG1e2X2yV9c25rOZP2cdrTTq3HBq3t9Ok8s79937W/bxz7PcdFrXNuT3/29FPr+nuO81uPy3rGc9I+l7X2fU8/NeXZuW+aop16xklrn7Ye67wkQ9Dt7UuPazN8LlnUs2/siQW07kc/mPbjsqZ26un7jrQfKaX8s4X3vXus03lJvmCpnN+wkO66heV3SPIDC6//Q5KXl1L+qJTy3PHx0gxn/T55Kc/W+fk9pZR7L6z/7zOcvXlaki/K8bo+u3Qcl/WMkxPVHSKDB+2R4VT194yPb8zwIflPMlyw8QlbvOeMDAcZz8oW1+7KcMbbNRlOn3xShoHxSxmivUdXpL9FhgjvH2Q4Xbe1rH+8XNYsfTuxQ/1vlOTHkrxjfHwqQ+T+N5OcuZT2FUuPzx6Xn3A2ztL7vibDqe+r/vetYxv98bj+rxmXn57kNxfS3SfJazP8hPhl4+PKDMGW+y7l+ZIsnbU0Lv8vST618PqPsnQtg3H5nXLiWSs97dTU/mN9jl2TbfG6ObfJid9I/GnGsycXlh3JcMrvJ1vWfWxdS+1xvy3SPmM39W/NM8lNtkhzWpauz7PwvzuO4+A+Se64RZonZ/hW5ZcyfGPx+IXx9Oe7zLNpjPY8MlzL4ZoMPw+5ZmFbumWWvuVpLedC2s/PsHP7xgyn1m93bZaWuay1T89NcvW4XT13fLx0XHbu0vuatpHtxnKSuy0ta93ue+aSpu1uXP4rSR6wRXkX57LbJPnVDGeXvjbDAeg1GYLQX7z0vqb9yHbtmRO3++bxnI45cot13zbDzxgWl923pf1bx11vn47pW/a3PeO5dT/WvA9N41zWk2dn/VuPN5qPdVr7tLOdWveNTfXpafuecZK+bXTLtEvpHtSSbhf1b9ru03dc1Do/N82j4+tHZDgL/qELy07J0vFF+ubcpnL2jNPOdro2DccGne00Rd/vy/5+cey31n1c1jrn9vRnTz+1rr/5OH/hf9sel6VvPPfMZa1939NPXccQadg3rWinZ+9DO/WMk6Y+TedxScsjQyCwdd+45Xa3It/W/WjPcVnX2O/s+23TJvniDAHK5eU3yvCz/VV5tnwePCXJ/TPMo984Pj/h2rpp/zxwRsZrCq5I9xVLr3s+u/QclzWPk1WPjbvGX5KU4VphpQ7XATiS4ZTbd9UhQrzd+74mQ8f88Bb//5wkqcN1uW6b4ToR76i1vm6bPL84wwel5+y2rKWUW9aFO8O0Gs+2OlJrXf62cKf3nZrhQOsfGtKeULbxW7PPTXJ1Hb5p2e79d8rC3XRqrX+7Is3Nkk9/q7D8vzvXWt+1wzpukeH06b/b4v/btlNr+5dSblJr/acVy0/LMJm/eWHZGRm+PVlV36+otb5qfH6PWuvbdlr3Xux2nHTkf1z7jd+GPCfDzuJY352R4Zun76m1vmHp/V+QIfj1lrrFHUWX8rwuwzda2+XZM0bvmOPH6Hu2S7/03ptn2OH89Ypy7lj33a5/p7mso/ynZLiY76fXn+Fb3k8updvVHLVdHr3bfeNc0rTd7bL8t05ytwwHV9dt1U8t+5He7b5nPG/x/pVzZM/Ya2n/XZSrK8+G/W3TeB7T7rpNt9qHtsxlvXkupdnz8caYrvtYZzdW1alnG+051utp+5Zx0rON9s6Prdvdbo91F96/5bFRw3HRno7LtilTz5yz45y7H+Ucx8NNV217ezjOPu7YoNcUfd+xv9/TcelWdW+Zc3fbny391Lj+5uP8FWl2+ozZegyx6/3I+P5tPxMtpGseow2fs7bdNy2l3XM79YyT3j7dy7FOx+fJVfvG5v3IFMe6ux37nX2/0zHMjvuHHT5n/b+14Xp3+/GZpjXP1vHUely217JvXOCvDKdCfqKOBS/DKd33SXJFrfWPGt7/PbXWn29c145py/ATtntkuCbeB7dIc6N64sUlT6u1vnc36aZMu+K9x138c8X/V9Z/m366vC7d7rq1T3v6fqq0K+q/5/FUSik58WDsdcfKsyL9ORl+AvLJDNcQ2PLAoCftivfesyX98hgpw0Va/22t9bVL6e6f5BfrwoVYx+U71r8nz47x9CUZrhnYGqDc73LeO30B0jOTfLjW+sEy/PTnnCRvrbW+ZSldz9ifbOyteO+2c8lS2taxd0K6njqNH4RSa/3U2G5fmOGajO/fQ55N/bTifdvOJbtp+23m5+3G/nEHTqWUe9VaL9tpXQvpu/c3O+1HO/ahq9r+ylrr5ftR1pb9/VL65v3DmL5p3G+XtmWctI7nXfR90xht3e6W3rPTGGne7jq2++5yblfWnjm/t+23Kcuu59Jx+Y7bSOO+8d6ZYH+3orxbjpPdHus1jL3W8bSrOi3lset+WvGeLeenqfZjW7xn1X68ZR5r3kY60zZv91PMJR159tSpqz875vLWdF3Hmgvv2+pzU+++qWf/sG/HuVON0TF99zbaMJe1zOU9dWr9PHbvNAbzej9jblGuHT+P7OL4bWWeHcevuzreWMpj5/193cPpgut4ZLx4/Pj8PyZ5dZIfzXD69n9dSvv9Kx7vPfZ8h7Q/sCptkp9feP6ADKeMvyLDxSofsZTnV2Y4O+m9GU7xPGvhf2/oTTdV2i3a6VgbvH8pz6b6d/ZTU9op8uxcf9MY6RxPD8tnfn7xy+Pj2M8vHraU5wMzXDvtTzLcmOIlSV6V4VTyu+w27Tbb2ju2qc92Y+Qvt8nz6qXXTfXvzHO7/vyphXRvTPJlK/K7f068ucYU5exZ/1Mz/EzjrRmuDfjWDKemX75i7LXWf9/HXs84aR17Pek66/T1GX5K9e4MP7V+bYbT669L8nW7zLOpn7Zon63mkp7tvnV+7hl7n0zylxnuVn32Nn3Rs29qLWdPnj3bSOu+sWd/39ynex33W4z91m20Zzy39n3PGP36tG13PW3f0/et629Kt+7tbr/G1Irx1LqNtO4be+re058946R139iTZ+t4aq7TRP3U83lo3/djrXVK31zSvI20pm3tzwnnkp60rXXq2Z5a9yM9/dQ6R/SM0Z6+bx3P+36c21nOKerUM5e19lNPOVs/X78x7fuHps9Znf3U+pm9J8+e49evT+N2v0N777i/b8roID1y/N2XLklys/H5kZx455OPZLjV8dMy3O3kaIaN+WhO/M10U9ocvyN9RZL7jM8/Nyf+Xv/iDLeSTpJvGjeU+4+vL+1NN1XaDBeZ/PGFei8+PriUZ1P9O/upKe0UeXauf4rxdGUWJoOF5XfLwh2ujvVZPnM3rrsl+b3x+UOTvGw3aZM8c4vHz2X4Jmk3Y+SZSf53hmuq/Ivx8a3jsmctpW2qf2eerf3ZE6Sbopw96788w5217jCOrcW7si3fEbG1/vs+9nYxTlrGXlO6XdbpTuP/Ppzk88bld83xc1lPnk39lL65pGe7b52fe8bepRm+efyJDAeAb8pw0HnWUrqefVNrOXvy7NlGWveNPfv71jm/Zzz3pG3dRnu3kZa+7903tWx3PW3f0/et629Kt+7trmecdI6n1m2kdd841f6uZ5y07ht78mwdTz11mqKfevY5U+zHWsdo71zSuo30zGWt2/0Uc0lv2pY69c6PLfuRnn5qnSN6j4ta+751PLfWvec4d9/HaGedeuay1n7qKecUn8eaPmd19lPr8VtPnj3Hrz3bffP+YWV77pTgoD0yRIu/cHz+0nwmknzTnDiBnZnhQqLPyGcuWHnNFvk2pV3aiJZvH78cwV2OUn9BhruFfv1SPk3ppko7tunKC5cmeedu6t/ZT01pp8izc/1TjKe/zHhL+qXlN86Jk93iJHnqUnsv32K8KW2Gye4JSR674vHe3YyRcdnDM5y2/Yfj4zlZ+nZpF/VvzbO1P7uCdBOUs2f9x271fmqSv8vxF97d7Xje97HXM046xl5Tul3U6dJt2vANu8yzqZ/SN5f0bPet83PP2Fver9wvw0War0vy6oXlPfum1nL25NmzjbTuG3v2961zfs947knbuo32jOfWvu8Zo63bXU/b9/R96/qb0q17u+sZJ53jqXUbaRpPnXXv6c+ecdK6b+zJs3U89dRpin7q3ufsVNbOPFvH6K72dw3bSOtc1tSfnX2/73l21qln7LXuR3r6qXWO6BlPPX3fOp6nOM7d9zHaWaeeuay1n3rKue+fx8b0O37O6uyn1uO3njx7jl8vXdV/W7R38/5h1eNINs+/S/KCUsqbMgz2S0opf57hNso/uZiwDrc//+ZSynlJ/riU8j+2yrQj7T1LKZclKUnOKqXcrtb6gfG32TdeSvuJUsqd6nghx1rr5aWUB2c4ffif7SLdVGkfn2Sr35Cfs8v6N/dTR9op8mxOO9F4el6Si0spL8xw6nUyXFviURlO2150SSnlVzLczemRGW8bX4YL9J66y7QXZ5hkXr1csFLKjy287BkjqcO1G3a85mY66t+RZ2t/PqmU8vAMp1UvXs/i2bXWC6cuZ+f631BK+c0M3+a9PMmvleG29F+V4Y5R3fXvqVPax1PPOGkde63peuuUUsoptdZPJfnOhWWn5vi5rCfPpn7qmUvSt903zc+dY68svqjDhYZfV0r5gST/cuFfPfum1v1IT54920hrvs37+44+7RnPPWlbx0nPeG7t+54x2rrd9Rxr9fR96/qb07WWdaLtLplmLm3dRprG04T7u55x0rpv7MmzdZz01Gnf+6lznzPFfqy1Tj1zSc820py2Y7ufYi7pSdtap56x19r+Pf3UOkf0jKeevm+t/xTHuZOM0Y469cxlrccGPeWc4vNY6+es5n7qGHs9fd9z/Nqz3ffsH06wcTf3SD7dEA/LcIHKIxmizBfVbS62XYYLWh7N8Bvy5YHZnLaUctel5O+utX68DHe9+Ze11t9dSPuQJNfXWt+0lMdtk3xvrfUnetJNmbZVZ/2b+6k17RR59qYd0+/LeBr/f3aGncziZHdBrfWKpXQ3SvLdSc7OcGr182qtnyzD3a0+q9b69t60ZbjL2cdqwx2eW5Xh7mo/lGECv2OSmmHC/4MM19H5YG/9d5Fn9xzRUK99L2fHuo8k+eYxvxcn+bIkj85wvY5n11o/upS+dXva97HXUaemsdc7Rjvq9KVJ3lxr/djS8rOSPKDW+hu7yLOrn8b37DQ/9Gz3zfNzq1LKt9Vaf7MhXc++qamcnXk2t33HvnlX7bnDMUTzeO5M2zNOWsdza9/3rLtpu+s81ujp+9b198wPa9vuxrT7Ppd2bntN46lVZ392tX3LvrFz7LWOp546TdJPC//baZ8zxX6sdYz2zCU920jrXNaz3U8xl/Skba1Tz9hr/ezQdUzYO0c0jKeevm+q/0THufs+Rjvr1Ds/tnzOaS7nmH5fP49N9TlrIf/mz/c75NOzD+3Z7vf0uX0jA3/sr4WN6OuTfFb2eSNi8/WMkVLKRRm+Mfu1On7TUYbbmT8uyVfVWh+2i/VPkee+7zx6yjn1zmsdzCWbYY5jDw66w7zdHea6A/PhOHf/9ewfWj9nTdFPs+j7usNvgQ/aI8ktkzw9w4UtP5Tk+iSvSfK4k5F2U/LsXP9FSZ6S5E4Ly+40Llu+SOthbqeDkudb9ivtRGPkqm2236t2uf4p8tyqTk9dUacpyrkf63/sQRp7nePkMG33j92HPHu2+3WM/Y1o+5nUaS/b6JR12s8xOse+3/ft7gDU6WTWfdf9eYDbaZPq9NiTWM59m0sOcN+vu07bjb3W/chBm/P3Yzyf9OPcDd1G17VvbPqcNUU/HfC+PyHtqsfGnfFXSvmDJL+X4Xbb35Lh9+0vzHB76HfVWn94yrSbkmfn+q+qtX7eFu193P8OeTttRJ5TrL9zjLxszO/Xaq3vGZfdMcO3MQ+ttT5kF+WcIs8pxn1POfd9/Z1lPbRzybrXfwDqtBH9dADaaSPyXPf6NyXPda9/pnP+RtR9U9ppk9a/KXmue/3qNK92Mpetfd/Y9DlrouPcjen7LdWG6OBBeuTEu6RcPP49Jclbp067KXl2rv9lSf5TkjsuLLtjhgj2nxyUOh2AdtqIPKdYf+cYuV2GOyO9NcPt0N+f4Tbxz0hy+12Wc4o8pxj3PeXc9/Wve+xN1KYbsY2sO8859tMBaKeNyHPd69+UPNe9/sxzzt+Ium9KO23S+jclz3WvX53m1U4xl61739j0OWuKftqkvt/qcUo2z0dLKQ9IklLKIzPeXaUOd0IpJyHtpuTZk/Zbk9whyZ+VUj5QSnl/hrsZ3T5DNPmg1Gnd7bQpeU6x/uYxUmv9QJL/meSJSe5Sa719rfXza61PyXDb9+5yTpFnT50mKucU211P2sM8l6x7/euu06b007rbaVPyXPf6NyXPda9/jnP+ptR9U9ppk9a/KXmue/3qNK92Mpetcd/Y8Tlrin7apL5fbafI4EF7JLlXktdliPL+RZJ7jMtPT/KkqdNuSp67SHvPJA9Jcsul5ecelDqtu502Jc8J1986Rp6U5Kokv5/k2iTnLfzvDbss577nOdG4by7nFOs/IGPvwM8l617/uuu0Kf207nbalDzXvf5NyfOArH9Wc/6m1H3D2mkj1r8pea57/eo0y3Yyl61v39jzeXCKftqIvt/qse0/N+2R5PHrTLspeS6n7dmIDmqd9P206++caN98bEJMclaSS5I8eXx96W7KOVGe+z7ue8p5sre7kzH25jCXrHv9J6NOc+gnfb8Z69+UPE/G+uc458+h7gepneay/k3Jc93rV6fNaydz2f7n2dOmafycNUU/zaHvmzLalEeSd6wz7abkuZy2dSM6yHXS99Ouv2eMJLl86fUtk7w0yU8neeNuyjlRnvs+7nvKebK3u5Mx9uYwl6x7/eb8g9NOc8hz3evflDxPxvrnOOfPoe4HqZ3msv5NyXPd61enzWsnc9n+59nTpmn8nDVFP82h749kw5RSLtvqXxkusDhp2k3JszPtKbXWv0+SWuu1pZQHJXlxKeWuWfq9+GFup03Jc6L1N4+RJO8ppdy71vrGMf3fl1K+NsnzknzRLus0RZ77Pu57yjnR+tc99jZiLln3+tddp2xIP627nTYlz3Wvf1PyPADrn92cvyl170m7KXmue/2bkue6169Os2snc9k+55lpPmNO0U8b0/db2bjAX4ZKfXWG3zYvKklefRLSbkqePWl7ghWHuZ02Jc8p1t8zRh6T5IbFBbXWG5I8ppTyi7ss5xR5TjHue8o5xfp70h7muWTd6193nTaln9bdTpuS57rXvyl5rnv9c5zzN6XuU9Rp3Xmue/2bkue6169O82onc9l6942tn7Om6KdN6vuVNjHw95IMp1m+cfkfpZRXnoS0m5JnT9qeYMVhbqdNyXOK9TePkVrrdct5LfzvVbsp5xR5ZoJx31nOKba7nrSHeS5Z9/rXXadN6ad1t9Om5Lnu9W9Knute/xzn/E2pe0/aTclz3evflDzXvX51mlc7mcvWuG/s+Jw1RT9tUt+vVOrwm2AAAAAAYEZOWXcBAAAAAID9J/AHAAAAADMk8AcAAAAAMyTwBwBAk1LKWaWUt3S+55GllKeOz3+slPKD4/PHlVI+Z4pyAgAwEPgDAOA4pZQj+5VXrfWCWutPrfjX45II/AEATEjgDwDgkCmlPL2U8h8WXv9EKeXJpZT/U0q5IMkV27z9SCnlBaWUK0spLy6l3HzM49pSymnj83NKKa8cnz+ulPKspfV/U5JzkryglPLGUsrN9reGAAAkAn8AAIfR85I8JklKKackeVSS65LcJ8mTa6332Oa9n5fk52utn5/kw0m+p3fltdYXJ7kkyb+utd671vqPvXkAALAzgT8AgEOm1nptkveVUr4kycOSXJrkfUleV2v96x3e/s5a66vG57+R5AGTFRQAgD3Zt+u3AACwUX45w3X27pThDMAk+WjD++oWr2/IZ75UvuleCwcAwN454w8A4HD6vSTnJvnSJBd1vO/MUsqXj8+/LclfjM+vTXLf8fk3NuTzkSS36lgvAACdBP4AAA6hWuvHk7wiyYtqrZ/seOtVSb63lHJlktsl+YVx+flJfraUckmSlvx+Nclz3NwDAGA6pdblX2sAADB340093pDkm2utf7nu8gAAsP+c8QcAcMiUUs5OcnWSlwv6AQDMlzP+AAA4TinlDklevuJfD661vu9klwcAgN0R+AMAAACAGfJTXwAAAACYIYE/AAAAAJghgT8AAAAAmCGBPwAAAACYof8f/bZuDaPADnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT UNIVARIATE SIMULATION FOR ALL THE YEAR ###\n",
    "\n",
    "test_diff.sort_values().plot.bar(figsize=(22,6), color='green')\n",
    "plt.xlabel('yr_built'); plt.ylabel('p-value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "30f58956",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T21:01:00.149673Z",
     "iopub.status.busy": "2023-01-08T21:01:00.149172Z",
     "iopub.status.idle": "2023-01-08T21:12:26.283385Z",
     "shell.execute_reply": "2023-01-08T21:12:26.282192Z"
    },
    "papermill": {
     "duration": 686.154348,
     "end_time": "2023-01-08T21:12:26.287491",
     "exception": false,
     "start_time": "2023-01-08T21:01:00.133143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [11:23<00:00,  1.46it/s]\n"
     ]
    }
   ],
   "source": [
    "### MULTIVARIATE SIMULATION FOR A SINGLE YEAR ###\n",
    "\n",
    "year = 2015\n",
    "\n",
    "cv_scoring = lambda x,y: np.mean(cross_val_score(\n",
    "    RandomForestClassifier(10), \n",
    "    x, y, cv=5, scoring='roc_auc', n_jobs=-1, \n",
    "    error_score='raise'\n",
    "))\n",
    "\n",
    "observed_score = cv_scoring(\n",
    "    df.drop(['yr_built','date','id'], axis=1), \n",
    "    (df['yr_built'] == year).astype(int)\n",
    ")\n",
    "\n",
    "sim_scores = np.asarray([\n",
    "    cv_scoring(\n",
    "        df.drop(['yr_built','date','id'], axis=1), \n",
    "        (df['yr_built'] == year).sample(frac=1).astype(int)\n",
    "    )\n",
    "    for i in tqdm(range(1_000))\n",
    "])\n",
    "\n",
    "p_value = np.mean(sim_scores >= observed_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d7f5711",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-08T21:12:26.438321Z",
     "iopub.status.busy": "2023-01-08T21:12:26.437855Z",
     "iopub.status.idle": "2023-01-08T21:12:26.699023Z",
     "shell.execute_reply": "2023-01-08T21:12:26.697684Z"
    },
    "papermill": {
     "duration": 0.339554,
     "end_time": "2023-01-08T21:12:26.701744",
     "exception": false,
     "start_time": "2023-01-08T21:12:26.362190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E/fffn/POO48PfvCDHHTQQRxxxBGMGDGCoUOH8p3vfGepYw866CAWLlzIwIEDOfvssxd3MwGMGzeOww47jI9+9KP06dNncX1H9wtw1113ceihh3YYo9QdRNv/6TTl5BFnAf8LeBO4DTgLeKC0ohAR/YBfZeagiPgzcFBmzijb/gp8JDNfWtb5R4wYkU2bq+EJc6U1Yqfm/f3qSh5//HEGDhzY6jC0imbPns1JJ53E7bff3upQlumYY45h3Lhx7LDDDktt8++f1phZs9rL22yzxk8fEVMyc0RH25rZJbQFtVaT7YBtgI2Ag9bAecdExOSImDxnzpzVPZ0krVCfPn047bTTKj1x3FFHHdVhsiKtUdts0750smZ2CX0C+FtmzsnMd4Hrgb2BzUsXEcC2wMxSngn0AyjbN6M2+PZ9MnN8Zo7IzBG9evVqYviS1G7kyJFLTRxXFeuvvz4nnXRSq8OQmqqZCctzwJ4RsWEZi3IA8BhwF3Bs2WcUcGMp31TWKdvvzGb2V0mdzL/OagX/3qm7aFrCkpl/oDZ49iFqrzSvA4wHvgb8U0Q8Re3V5svLIZcDW5X6fwI6nhZS6oJ69uzJ3Llz/eWhTpWZzJ0711edtebMmtW+dLKmDrptNgfddgEOugXg3XffZcaMGQ3PpyGtKT179mTbbbf1dWetGfUv7zYhf1jeoNumThwnqWa99dZju+22a3UYktRlOTW/JEmqPBMWSZJUeSYskiSp8kxYJElS5ZmwSJKkyjNhkSRJlWfCIkmSKs+ERZIkVZ4JiyRJqjxnupUkSY1p4ed8bGGRJEmVZ8IiSZIqz4RFkiRVnmNYJElSY6ZMaS8PH96plzZhkSRJjRkxor3cyQNw7RKSJEmVZ8IiSZIqz4RFkiRVngmLJEmqPBMWSZJUeSYskiSp8kxYJElS5TUtYYmIHSNiat3yWkR8OSK2jIjbI+LJ8nOLsn9ExCUR8VRETIuIYc2KTZIkdS1NS1gy8z8zc2hmDgWGAwuAG4CzgUmZuT0wqawDHAxsX5YxwKXNik2SJHUtndUldADw18x8FjgSmFDqJwBHlfKRwBVZ8wCweUT06aT4JEnSivTp0750ss6amv844P+Vcu/MnF3KzwO9S7kvML3umBmlbjaSJKn1Zs1q2aWb3sISEesDRwA/W3JbZiawUh8jiIgxETE5IibPmTNnDUUpSZKqrDO6hA4GHsrMF8r6C21dPeXni6V+JtCv7rhtS937ZOb4zByRmSN69erVxLAlSVJVdEbCcjzt3UEANwGjSnkUcGNd/UnlbaE9gXl1XUeSJGkt1tQxLBGxEfBJ4PS66nHAdRFxCvAsMLLU3wocAjxF7Y2i0c2MTZIkraSbb24vH354p166qQlLZr4BbLVE3Vxqbw0tuW8CZzYzHkmStBqOOKK9nCs1BHW1OdOtJEmqPBMWSZJUeSYskiSp8kxYJElS5ZmwSJKkyjNhkSRJlWfCIkmSKs+ERZIkVZ4JiyRJqrymznQrSZK6kWHDWnZpExZJktSYKVNadmm7hCRJUuWZsEiSpMozYZEkSZXnGBZJktSY8ePby2PGdOqlTVgkSVJjTj+9vdzJCYtdQpIkqfJMWCRJUuWZsEiSpMozYZEkSZVnwiJJkirPhEWSJFVeUxOWiNg8IiZGxBMR8XhE7BURW0bE7RHxZPm5Rdk3IuKSiHgqIqZFROu+sCRJkiql2S0sFwO/zsydgCHA48DZwKTM3B6YVNYBDga2L8sY4NImxyZJkrqIpiUsEbEZsC9wOUBmvpOZrwJHAhPKbhOAo0r5SOCKrHkA2Dwi+jQrPkmS1HU0c6bb7YA5wE8iYggwBTgL6J2Zs8s+zwO9S7kvML3u+BmlbjaSJKn1DjusZZduZsKyLjAM+MfM/ENEXEx79w8AmZkRkStz0ogYQ63LiP79+6+pWCVJ0orcfHPLLt3MMSwzgBmZ+YeyPpFaAvNCW1dP+fli2T4T6Fd3/Lal7n0yc3xmjsjMEb169Wpa8JIkqTqalrBk5vPA9IjYsVQdADwG3ASMKnWjgBtL+SbgpPK20J7AvLquI0mStBZr9tea/xG4KiLWB54GRlNLkq6LiFOAZ4GRZd9bgUOAp4AFZV9JkqTmJiyZORUY0cGmAzrYN4EzmxmPJElaDWPHdlzuBM1uYZEkSd3Ft7/dXu7khMWp+SVJUuWZsEiSpMozYZEkSZVnwiJJkirPhEWSJFWeCYskSao8ExZJklR5JiySJKnyTFgkSVLlOdOtJElqzGmntezSJiySJKkx48e37NJ2CUmSpMozYZEkSZVnwiJJkirPMSySJKkxY8a0lzt5PIsJiyRJasxll7WXOzlhsUtIkiRVngmLJEmqPBMWSZJUeSYskiSp8kxYJElS5ZmwSJKkymtqwhIRz0TEIxExNSIml7otI+L2iHiy/Nyi1EdEXBIRT0XEtIgY1szYJElS19EZLSx/n5lDM3NEWT8bmJSZ2wOTyjrAwcD2ZRkDXNoJsUmSpC6gFV1CRwITSnkCcFRd/RVZ8wCweUT0aUF8kiSpYlY4021ETAF+DFydma+s5PkTuC0iEviPzBwP9M7M2WX780DvUu4LTK87dkapm40kSWq9c85p2aUbmZr/s8Bo4MEyDuUnwG2ZmQ0c+7HMnBkR/wW4PSKeqN+YmVmSmYZFxBhqXUb0799/ZQ6VJEmrY+zYll16hV1CmflUZv5PYAfgamqtLc9GxLcjYssVHDuz/HwRuAHYA3ihraun/Hyx7D4T6Fd3+Lalbslzjs/MEZk5olevXisKX5IkdQMNjWGJiMHABcD5wM+BzwCvAXcu55iNImKTtjJwIPBn4CZgVNltFHBjKd8EnFTeFtoTmFfXdSRJktZijY5heRW4HDg7M98um/4QEXsv59DewA0R0XadqzPz1xHxIHBdRJwCPAuMLPvfChwCPAUsoNYNJUmS1NAYls9k5tMdbcjMY5Z1UDlmSAf1c4EDOqhP4MwG4pEkSa1w+OHt5Ztv7tRLN5KwnBoR52XmqwBlord/zsxvNDUySZJULbfc0rJLNzKG5eC2ZAWgvNp8SNMikiRJWkIjCUuPiPhA20pEbAB8YDn7S5IkrVGNdAldBUyKiJ+U9dG0z1QrSZLUdCtMWDLz3IiYRvtA2X/LzN80NyxJkqR2jbSwkJm/An7V5FgkSZI6tMIxLBFxTEQ8GRHzIuK1iHg9Il7rjOAkSZKgsRaW84DDM/PxZgcjSZLUkUbeEnrBZEWSJLVSIy0skyPiWuAXQNu0/GTm9c0KSpIkqV4jCcum1L7tc2BdXQImLJIkrU3+4z9adulGXmv2I4SSJAnGjGnZpRt5S2iHiJgUEX8u64Mjwu8ISZKkTtPIoNvLgK8D7wJk5jTguGYGJUmSVK+RhGXDzPzjEnULmxGMJElSRxoZdPtSRPwdtYG2RMSxwOymRiVJkqpn+PD28pQpnXrpRhKWM4HxwE4RMRP4G/C5pkYlSZKq56GHWnbpRt4Sehr4RERsBKyTma83PyxJkqR2K0xYIuJbS6wDkJn/2qSYJEmS3qeRLqE36so9gcMAp+qXJEmdppEuoQvq1yPiO8BvmhaRJEnSEhp5rXlJGwLbrulAJEmSlqWRMSyPUF5pBnoAvQDHr0iSpE7TyBiWw+rKC4EXMrPhieMiogcwGZiZmYdFxHbANcBWwBTgxMx8JyI+AFwBDAfmAp/NzGcavY4kSeq+GukSer1ueRPYNCK2bFsaOP4s3j9I91zgosz8MPAKcEqpPwV4pdRfVPaTJElqKGF5CJgD/AV4spSnlGXy8g6MiG2BQ4EflfUA9gcmll0mAEeV8pFlnbL9gGh7h1qSJK3VGukSuh24ITNvBYiIg4GjMvP0Bo79LvA/gE3K+lbAq3VdSjOAvqXcF5gOkJkLI2Je2f+l+hNGxBhgDED//v0bCEGSJK0RN93Usks3krDsmZmnta1k5q8i4rwVHRQRhwEvZuaUiNhv1UN8v8wcT+1TAYwYMSJXsLskSVpTDj+8ZZduJGGZFRHfAH5a1k8AZjVw3N7AERFxCLUJ5zYFLgY2j4h1SyvLtsDMsv9MoB8wIyLWBTajNvhWkiSt5RoZw3I8tVeZbwCuL+XjV3RQZn49M7fNzAHAccCdmXkCcBdwbNltFHBjKd9U1inb78xMW1AkSVJDM92+DJwVERtl5hsr2r8BXwOuiYh/B/4EXF7qLweujIingJepJTmSJEkNTRz3UWpv+WwM9I+IIcDpmfnfGr1IZt4N3F3KTwN7dLDPW8BnGj2nJEnqZNts016e1cjokDWnkTEsFwGfotZlQ2Y+HBH7NjUqSZJUPbNnt+zSDX1LKDOnL1H1XhNikSRJ6lAjLSzTS7dQRsR6LD1zrSRJUlM10sLyBeBMahO7zQSGlnVJkqROsdwWlvLhwovL68iSJEktsdwWlsx8D/hQRKzfSfFIkiQtpZExLE8D90XETcDieVgy88KmRSVJklRnmS0sEXFlKR4B3FL23aRukSRJ6hTLa2EZHhHbAM8B3+ukeCRJkpayvITlh8AkYDtgcl19AAn81ybGJUmStNgyE5bMvAS4JCIuzcwzOjEmSZJURZMnr3ifJmnk44cmK5IkCYYPb9mlG5qaX5IkqZVMWCRJUuWZsEiSpMprZOI4SZIkiGgvZ3bqpW1hkSRJlWfCIkmSKs+ERZIkVZ4JiyRJqjwTFkmSVHkmLJIkqfKalrBERM+I+GNEPBwRj0bEt0v9dhHxh4h4KiKujYj1S/0HyvpTZfuAZsUmSZK6lma2sLwN7J+ZQ4ChwEERsSdwLnBRZn4YeAU4pex/CvBKqb+o7CdJktS8hCVr5pfV9cqSwP7AxFI/ATiqlI8s65TtB0TUz1AjSZLWVk0dwxIRPSJiKvAicDvwV+DVzFxYdpkB9C3lvsB0gLJ9HrBVM+OTJEldQ1On5s/M94ChEbE5cAOw0+qeMyLGAGMA+vfvv7qnkyRJjZo5s2WX7pS3hDLzVeAuYC9g84hoS5S2BdrufibQD6Bs3wyY28G5xmfmiMwc0atXr2aHLkmS2myzTfvSyZr5llCv0rJCRGwAfBJ4nFricmzZbRRwYynfVNYp2+/M7OQvK0mSpEpqZpdQH2BCRPSglhhdl5m3RMRjwDUR8e/An4DLy/6XA1dGxFPAy8BxTYxNkiR1IU1LWDJzGrBbB/VPA3t0UP8W8JlmxSNJklbTrFnt5U7uFmrqoFtJktSN9O3bXu7kURtOzS9JkirPhEWSJFWeCYskSao8ExZJklR5JiySJKnyTFgkSVLlmbBIkqTKM2GRJEmVZ8IiSZIqz4RFkiRVnlPzS5KkxnTydPz1bGGRJEmVZ8IiSZIqz4RFkiRVnmNYJElSY6ZMaS8PH96plzZhkSRJjRkxor3cyQNw7RKSJEmVZ8IiSZIqz4RFkiRVngmLJEmqPBMWSZJUeSYskiSp8pqWsEREv4i4KyIei4hHI+KsUr9lRNweEU+Wn1uU+oiISyLiqYiYFhHDmhWbJEnqWprZwrIQ+OfM3BnYEzgzInYGzgYmZeb2wKSyDnAwsH1ZxgCXNjE2SZLUhTQtYcnM2Zn5UCm/DjwO9AWOBCaU3SYAR5XykcAVWfMAsHlE9GlWfJIkqevolJluI2IAsBvwB6B3Zs4um54HepdyX2B63WEzSt1sJElS6/VpXTtC0xOWiNgY+Dnw5cx8LSIWb8vMjIiVmts3IsZQ6zKif//+azJUSZK0PLNmtezSTX1LKCLWo5asXJWZ15fqF9q6esrPF0v9TKBf3eHblrr3yczxmTkiM0f06tWrecFLkqTKaOZbQgFcDjyemRfWbboJGFXKo4Ab6+pPKm8L7QnMq+s6kiRJa7FmdgntDZwIPBIRU0vdvwDjgOsi4hTgWWBk2XYrcAjwFLAAGN3E2CRJUhfStIQlM+8FYhmbD+hg/wTObFY8kiRpNd18c3v58MM79dKd8paQJEnqBo44or2cK/XOzGpzan5JklR5JiySJKnyTFgkSVLlmbBIkqTKM2GRJEmVZ8IiSZIqz4RFkiRVngmLJEmqPBMWSZJUec50K0mSGjNsWMsubcIiSZIaM2VKyy5tl5AkSao8ExZJklR5JiySJKnyHMMiSZIaM358e3nMmE69tAmLmuuJaHUE3cdO2eoIJK3tTj+9vdzJCYtdQpIkqfJMWCRJUuWZsEiSpMozYZEkSZVnwiJJkiqvaQlLRPw4Il6MiD/X1W0ZEbdHxJPl5xalPiLikoh4KiKmRUTrPlYgSZIqp5ktLP8XOGiJurOBSZm5PTCprAMcDGxfljHApU2MS5IkdTFNS1gy8x7g5SWqjwQmlPIE4Ki6+iuy5gFg84jo06zYJElS19LZY1h6Z+bsUn4e6F3KfYHpdfvNKHWSJEmtm+k2MzMiVnrqzogYQ63biP79+6/xuCRJ0jIcdljLLt3ZCcsLEdEnM2eXLp8XS/1MoF/dftuWuqVk5nhgPMCIESOcq1ySpM5y880tu3RndwndBIwq5VHAjXX1J5W3hfYE5tV1HUmSpLVc01pYIuL/AfsBW0fEDOAcYBxwXUScAjwLjCy73wocAjwFLABGNysuSZLU9TQtYcnM45ex6YAO9k3gzGbFIkmSuraWDbqVJEldzNixHZc7gQmLJElqzLe/3V7u5ITFbwlJkqTKM2GRJEmVZ8IiSZIqz4RFkiRVngmLJEmqPBMWSZJUeSYskiSp8kxYJElS5ZmwSJKkynOmW0mS1JjTTmvZpU1YJElSY8aPb9ml7RKSJEmVZ8IiSZIqzy4hqat4IlodQfewU7Y6AkmrwIRFkiQ1ZsyY9nInj2cxYZEkSY257LL2cicnLI5hkSRJlWfCIkmSKs+ERZIkVZ4JiyRJqjwTFkmSVHmVSlgi4qCI+M+IeCoizm51PJIkqRoq81pzRPQAfgB8EpgBPBgRN2XmY62NTFK34gR8a4YT8KmTVamFZQ/gqcx8OjPfAa4BjmxxTJIkqQIq08IC9AWm163PAD7SolgkSctjS9WaYUtVw6qUsDQkIsYAbXMDz4+I/2xlPF3U1sBLrQ5CPocK8BlUw1r8HCqT+K38M4imxP6hZW2oUsIyE+hXt75tqXufzBwPdO58wN1MREzOzBGtjmNt53NoPZ9BNfgcWq8rPIMqjWF5ENg+IraLiPWB44CbWhyTJEmqgMq0sGTmwoj4IvAboAfw48x8tMVhSZKkCqhMwgKQmbcCt7Y6jrWAXWrV4HNoPZ9BNfgcWq/yzyAyHaEsSZKqrUpjWCRJkjpkwtLNrOjzBhFxckTMiYipZTm1btuoiHiyLKM6N/LuY1WfQUQMjYj7I+LRiJgWEZ/t/Oi7j9X5t1C2bxoRMyLi+50Xdfeymv896h8Rt0XE4xHxWEQM6NTgu5HVfA7nlf8mPR4Rl0Q0513mhmSmSzdZqA1W/ivwX4H1gYeBnZfY52Tg+x0cuyXwdPm5RSlv0ep76mrLaj6DHYDtS3kbYDaweavvqSsuq/Mc6rZfDFy9vH1cmvcMgLuBT5byxsCGrb6nrris5n+TPgrcV87RA7gf2K9V92ILS/eyOp83+BRwe2a+nJmvALcDBzUpzu5slZ9BZv4lM58s5VnAi0CvpkXava3Wpz4iYjjQG7itSfGtDVb5GUTEzsC6mXk7QGbOz8wFzQu1W1udfwsJ9KSW6HwAWA94oSlRNsCEpXvp6PMGfTvY79Oly2FiRLRN1tfosVq+1XkGi0XEHtT+I/HX5oTZ7a3yc4iIdYALgK80P8xubXX+LewAvBoR10fEnyLi/PKBXK28VX4OmXk/cBe11t7ZwG8y8/FmB7wsJixrn5uBAZk5mForyoQWx7M2Wu4ziIg+wJXA6Mxc1IL41hbLeg7/Dbg1M2e0LLK1x7KewbrAPtSSxt2pdWec3IoA1xIdPoeI+DAwkNrM832B/SNin1YFacLSvazw8waZOTcz3y6rPwKGN3qsGrI6z4CI2BT4JfA/M/OBJsfana3Oc9gL+GJEPAN8BzgpIsY1N9xuaXWewQxgaunGWAj8AhjW3HC7rdV5DkcDD5QuufnAr6j9+2gJE5buZYWfNyj/997mCKCtee83wIERsUVEbAEcWOq0clb5GZT9bwCuyMyJnRRvd7XKzyEzT8jM/pk5gNr/4V+RmUu9WaEVWp3/Hj0IbB4RbWO49gcea3K83dXqPIfngI9HxLoRsR7w8bptna5SM91q9eQyPm8QEf8KTM7Mm4AvRcQRwELgZUoza2a+HBH/Ru0vN8C/ZubLnX4TXdzqPANgJLAvsFVEtNWdnJlTO/EWuoXVfA5aA1bzv0fvRcRXgEnlNdopwGWtuI+ubjX/LUykliw+Qm0A7q8z8+bOvoc2znQrSZIqzy4hSZJUeSYskiSp8kxYJElS5ZmwSJKkyjNhkSRJlWfCIkmSKs+EReqCIuJH5QNxa+Jcz0TE1ivY519W4bwnR8T3Vz2yzhERu0XE5a2OY1ki4rAyZ4a0VjNhkbqgzDw1Mztz5s+VTlg6S0Ss7gSY/wJc0oTzrim/BA6PiA1bHYjUSiYsUoVFxEYR8cuIeDgi/hwRny31d0fEiFKeX75m+2hE3BERe5TtT5fZK5dq7YiIWyJivw6u94uImFLONabUjQM2iIipEXFVqftcRPyx1P1H25d0I2J0RPwlIv4I7L2Me/p4OW5q+RLvJqX+axHxSLnXcaVuaEQ8UL4ie0P5bETb/X83IiYDZ0XE8Ij4bYn9N21TjUfElyLisXL8NR3EsgkwODMfLutjI+LKiLgPuDIiBkTEneX4SRHRv+zXu8TzcFk+2sG594iI+8s9/j4idlzRs4iIgyLioXLOSQBZm93zbuCwjv48pbVGZrq4uFR0AT4NXFa3vln5eTcwopQTOLiUbwBuA9YDhlD7gBzUptr+ft15bgH2K+VngK1LecvycwPgz8BWZX1+3bEDqX3ddb2y/n+Ak4A+1L490gtYH7iv/pp1x98M7F3KG1P7RMjBwO+BDZeIYxrw8VL+V+C7dff/f0p5vXJsr7L+WWrTjwPMAj5Qypt3EMvfAz+vWx9LbRr4DepiHVXKnwd+UcrXAl8u5R5tz2WJc28KrFvKn2i7zrKeRflzmw5sV/9nUMonAN9r9d9HF5dWLlVp8pTUsUeACyLiXOCWzPxdB/u8A/y6bv+3M/PdiHgEGLCS1/tSRBxdyv2A7YG5S+xzALWvuT5Y+8wLGwAvAh8B7s7MOQARcS2wQwfXuA+4sLTWXJ+ZMyLiE8BPMnMBLP621WbUkozfluMmAD+rO8+15eeOwCDg9hJPD2B22TYNuCoifkHti79L6gPMWaLupsx8s5T3Ao4p5SuB80p5f2pJGpn5HjCvg3NvBkyIiO2pJZXrdbBPvT2BezLzb+W89d/yehHYZgXHS92aXUJShWXmX4Bh1BKRf4+Ib3Ww27uZ2fZRsEXA2+XYRbR/4HQh7//33nPJk5RuiU8Ae2XmEOBPHe0HBDAhM4eWZcfMHLsS9zQOOJVaonNfROzU6LFLeKMunkfr4tk1Mw8s2w4FfkDtz/DBDsalvMnS9/gGqyAizqzr6toG+DfgrswcBBxed50VPosO9CyxSmstExapwsovvgWZ+VPgfGq/eFfFM8DQiFgnIvoBe3Swz2bAK5m5oCQRe9Ztezdqn5cHmAQcGxH/pcS4ZUR8CPgDtU/Rb1X2/cwy7unvMvORzDyX2tfBdwJuB0a3DSyNiC0zcx7wSkTsUw49EfhtB6f8T6BXROxVjl0vInaJiHWAfpl5F/C1cn8bL3Hs48CHO4qz+D1wXCmfALS1cE0CzijX6xERm2XmD+qSplnlejPL/ifXnfMZOn4WDwD7RsR2bX8GdcfsQK2LTlpr2SUkVduuwPkRsQh4l/JLchXcB/wNeIzaL+mHOtjn18AXIuJxaknAA3XbxgPTIuKhzDwhIr4B3FaSgneBMzPzgYgYC9wPvApMXUYsX46Iv6fWGvQo8KvMfDsihgKTI+Id4FZqb++MAn5YEpmngdFLniwz34mIY4FLSjfSusB3gb8APy11AVySma8ucewTEbFZRGySma93EOs/Aj+JiK9S6zpqu/5ZwPiIOAV4j9pzuX+JY8+j1iX0DWpv+rTp8Flk5pyoDXS+vvy5vgh8shzz98DXO4hPWmtEe0uyJK19IuK/A69n5o9aHUtHIqI3cHVmHtDqWKRWsktI0truUsq4n4rqD/xzq4OQWs0WFkmSVHm2sEiSpMozYZEkSZVnwiJJkirPhEWSJFWeCYskSaq8/x/vw+4DxD6nSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT MULTIVARIATE SIMULATION FOR A SINGLE YEAR ###\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "plt.hist(sim_scores, bins=5, color='gold')\n",
    "plt.axvline(observed_score, linestyle='--', linewidth=3, c='red', \n",
    "            label='observed score (roc-auc)')\n",
    "plt.title(f'year building: {year}')\n",
    "plt.legend(); plt.xlabel('simulated scores (roc-auc)'); plt.ylabel('frequency')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 794.834579,
   "end_time": "2023-01-08T21:12:29.399323",
   "environment_variables": {},
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